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Fawaz P, El Sayegh P, Vande Vannet B. Artificial intelligence in revolutionizing orthodontic practice. World J Methodol 2025; 15:100598. [DOI: 10.5662/wjm.v15.i3.100598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2024] [Revised: 10/07/2024] [Accepted: 12/18/2024] [Indexed: 03/06/2025] Open
Abstract
This analytical research paper explores the transformative impact of artificial intelligence (AI) in orthodontics, with a focus on its objectives: Identifying current applications, evaluating benefits, addressing challenges, and projecting future developments. AI, a subset of computer science designed to simulate human intelligence, has seen rapid integration into orthodontic practice. The paper examines AI technologies such as machine learning, deep learning, natural language processing, computer vision, and robotics, which are increasingly used to analyze patient data, assist with diagnosis and treatment planning, automate routine tasks, and improve patient communication. AI systems offer precise malocclusion diagnoses, predict treatment outcomes, and customize treatment plans by leveraging dental imagery. They also streamline image analysis, improve diagnostic accuracy, and enhance patient engagement through personalized communication. The objectives include evaluating the benefits of AI in terms of efficiency, accuracy, and personalized care, while acknowledging the challenges like data quality, algorithm transparency, and practical implementation. Despite these hurdles, AI presents promising prospects in advanced imaging, predictive analytics, and clinical decision-making. In conclusion, AI holds the potential to revolutionize orthodontic practices by improving operational efficiency, diagnostic precision and patient outcomes. With collaborative efforts to overcome challenges, AI could play a pivotal role in advancing orthodontic care.
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Affiliation(s)
- Paul Fawaz
- Faculty of Dentistry, Department of Orthodontics, University Lorraine, Nancy 54000, France
| | - Patrick El Sayegh
- Faculty of Dentistry, Saint Joseph University of Beirut, Beirouth 11042020, Lebanon
| | - Bart Vande Vannet
- Faculty of Dentistry, Department of Orthodontics, University Lorraine, Nancy 54000, France
- Institut Jean Lamour, Campus Artem (403), University Lorraine, Nancy 54000, France
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Tarazi A, Aburrub A, Hijah M. Use of artificial intelligence in neurological disorders diagnosis: A scientometric study. World J Methodol 2025; 15:99403. [DOI: 10.5662/wjm.v15.i3.99403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Revised: 12/03/2024] [Accepted: 12/23/2024] [Indexed: 03/06/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI) has become significantly integrated into healthcare, particularly in the diagnosing of neurological disorders. This advancement has enabled neurologists and physicians to diagnose conditions more quickly and effectively, ultimately benefiting patients.
AIM To explore the current status and key highlights of AI-related articles in diagnosing of neurological disorders.
METHODS A systematic literature review was conducted in the Web of Science Core Collection database using the following strategy: TS = ("Artificial Intelligence" OR "Computational Intelligence" OR "Machine Learning" OR "AI") AND TS = ("Neurological disorders" OR "CNS disorder" AND "diagnosis"). The search was limited to articles and reviews. Microsoft Excel 2019 and VOSviewer were utilized to identify major contributors, including authors, institutions, countries, and journals. Additionally, VOSviewer was employed to analyze and visualize current trends and hot topics through network visualization maps.
RESULTS A total of 276 publications from 2000 to 2024 were retrieved. The United States, India, and China emerged as the top contributors in this field. Major institutions included Johns Hopkins University, King's College London, and Harvard Medical School. The most prolific author was U. Rajendra Acharya from the University of Southern Queensland (Australia). Among journals, IEEE Access, Scientific Reports, and Sensors were the most productive, while Frontiers in Neuroscience led in total citations. Central topics in AI-related articles on neurological disorders diagnosis included Alzheimer's disease, Parkinson's disease, dementia, epilepsy, autism, attention deficit hyperactivity disorder, and their intersections with deep learning and AI.
CONCLUSION Research on AI's role in diagnosing neurological disorders is becoming widely recognized for its growing importance. AI shows promise in diagnosing various neurological disorders, yet requires further improvement and extensive future research.
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Affiliation(s)
- Alaa Tarazi
- School of Medicine, University of Jordan, Amman 11942, Jordan
| | - Ahmad Aburrub
- School of Medicine, University of Jordan, Amman 11942, Jordan
| | - Mohammad Hijah
- School of Medicine, University of Jordan, Amman 11942, Jordan
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Shankar R, Bundele A, Mukhopadhyay A. Effectiveness of Chatbot interventions for reducing caregiver burden: Protocol for a systematic review and meta-analysis. MethodsX 2025; 14:103272. [PMID: 40201159 PMCID: PMC11978356 DOI: 10.1016/j.mex.2025.103272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Accepted: 03/14/2025] [Indexed: 04/10/2025] Open
Abstract
This protocol outlines a systematic review and meta-analysis examining the effectiveness of fully automated, AI-driven chatbot interventions in reducing subjective burden among informal caregivers. We will search 8 electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, Cochrane Library, PsycINFO, Scopus) and grey literature sources from January 2010 to December 2024 for randomized controlled trials (RCTs) meeting predefined eligibility criteria. The primary outcome is caregiver burden, assessed using validated scales such as the Zarit Burden Interview. Secondary outcomes encompass caregiver mental health, quality of life, self-efficacy and care recipient status. Two reviewers will independently perform study selection, data extraction, risk of bias evaluation using Cochrane RoB 2 tool, and appraise certainty of evidence utilizing the GRADE approach. We will conduct random-effects meta-analyses, subgroup analyses, and meta-regression to compute pooled effect estimates and explore heterogeneity. If quantitative synthesis is precluded, narrative synthesis will be undertaken following SWiM guideline. Caregiver partners will provide input on interpretation and dissemination of findings.•Protocol adheres to PRISMA-P reporting standards and will be prospectively registered in PROSPERO•Graphviz code for replicating the systematic review methodology diagram is provided•Review will yield critical evidence to guide development and implementation of chatbots into caregiver support services.
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Affiliation(s)
- Ravi Shankar
- Research and Innovation, Medical Affairs, Alexandra Hospital, Singapore, Singapore
| | - Anjali Bundele
- Research and Innovation, Medical Affairs, Alexandra Hospital, Singapore, Singapore
| | - Amartya Mukhopadhyay
- Division of Respiratory and Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
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Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, Mondal S, Biswas UK, Jana M, Bhattacharjee S. The biomedical applications of artificial intelligence: an overview of decades of research. J Drug Target 2025; 33:717-748. [PMID: 39744873 DOI: 10.1080/1061186x.2024.2448711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 12/13/2024] [Accepted: 12/26/2024] [Indexed: 01/11/2025]
Abstract
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.
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Affiliation(s)
- Sweet Naskar
- Department of Pharmaceutics, Institute of Pharmacy, Kalyani, West Bengal, India
| | - Suraj Sharma
- Department of Pharmaceutics, Sikkim Professional College of Pharmaceutical Sciences, Sikkim, India
| | - Ketousetuo Kuotsu
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Suman Halder
- Medical Department, Department of Indian Railway, Kharagpur Division, Kharagpur, West Bengal, India
| | - Goutam Pal
- Service Dispensary, ESI Hospital, Hoogly, West Bengal, India
| | - Subhankar Saha
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Shubhadeep Mondal
- Department of Pharmacology, Momtaz Begum Pharmacy College, Rajarhat, West Bengal, India
| | - Ujjwal Kumar Biswas
- School of Pharmaceutical Science (SPS), Siksha O Anusandhan (SOA) University, Bhubaneswar, Odisha, India
| | - Mayukh Jana
- School of Pharmacy, Centurion University of Technology and Management, Centurion University, Bhubaneswar, Odisha, India
| | - Sunirmal Bhattacharjee
- Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India
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Hui ML, Sacoransky E, Chung A, Kwan BY. Exploring the integration of artificial intelligence in radiology education: A scoping review. Curr Probl Diagn Radiol 2025; 54:332-338. [PMID: 39379203 DOI: 10.1067/j.cpradiol.2024.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2024] [Accepted: 10/02/2024] [Indexed: 10/10/2024]
Abstract
BACKGROUND The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current landscape of AI integration in radiology education. METHODS The review process involved systematically searching four databases, including MEDLINE (Ovid), Embase (Ovid), PsychINFO (Ovid), and Scopus. Inclusion criteria focused on research that addresses the use of AI technologies in radiology education, including but not limited to, AI-assisted learning platforms, simulation tools, and automated assessment systems. This scoping review was registered on Open Science Framework using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping review. RESULTS Of the 1081 search results, 9 studies met the inclusion criteria. Key findings indicate a diverse range of AI applications in radiology education, from personalized curriculum generation and diagnostic support tools to automated evaluation systems. The review highlights both the potential benefits, such as enhanced diagnostic accuracy, and the challenges, including technical limitations. CONCLUSION The integration of AI into radiology education, which has significant potential to enhance outcomes and professional practice, requires overcoming existing challenges and ensuring that AI complements rather than replaces traditional methods, with future research needed on longitudinal studies to evaluate its long-term impact.
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Affiliation(s)
- Muying Lucy Hui
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Ethan Sacoransky
- School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Andrew Chung
- School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
| | - Benjamin Ym Kwan
- School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, ON, Canada
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Sun B, Vivekanantha P, Khalik HA, de Sa D. Several factors predict the achievement of the patient acceptable symptom state and minimal clinically important difference for patient-reported outcome measures following anterior cruciate ligament reconstruction: A systematic review. Knee Surg Sports Traumatol Arthrosc 2025; 33:1617-1632. [PMID: 39248212 DOI: 10.1002/ksa.12460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 08/25/2024] [Accepted: 08/27/2024] [Indexed: 09/10/2024]
Abstract
PURPOSE To summarize the predictors of the patient acceptable symptom state (PASS), minimal clinically important difference (MCID) and minimal important change (MIC) for patient-reported outcome measures (PROMs) following anterior cruciate ligament reconstruction (ACLR). METHODS MEDLINE, PubMed and Embase were searched from inception to 5 January 2024. The authors adhered to PRISMA/R-AMSTAR guidelines, and the Cochrane Handbook for Systematic Reviews of Interventions. Data on statistical associations between predictive factors and PROMs were extracted. Inverse odds ratios (ORs) and confidence intervals (reverse group comparison) were calculated when appropriate to ensure comparative consistency. RESULTS Thirteen studies comprising 21,235 patients (48.1% female) were included (mean age 29.3 years). Eight studies comprising 3857 patients identified predictors of PASS, including lateral extra-articular tenodesis (LET) (OR = 11.08, p = 0.01), hamstring tendon (HT) autografts (OR range: 2.02-2.63, p ≤ 0.011), age over 30 (OR range: 1.37-2.28, p ≤ 0.02), male sex (OR range: 1.03-1.32, p ≤ 0.01) and higher pre-operative PROMs (OR range: 1.04-1.21). Eight studies comprising 18,069 patients identified negative predictors of MCID or MIC, including female sex (OR = 0.93, p = 0.034), absence of HT autografts (OR = 0.70, p < 0.0001), higher pre-operative PROMs (OR = 0.76-0.84, p ≤ 0.01), meniscectomy (OR = 0.67, p = 0.014) and collision sports (OR = 0.02-0.60, p ≤ 0.05). CONCLUSION Higher pre-operative PROMs, age over 30, male sex, LETs and HT autografts predicted PASS achievement. Lower pre-operative PROMs, male sex, non-collision sports, and lack of meniscectomies predicted MCID/MIC achievement. This review provides a comprehensive understanding of the predictors of clinically significant post-ACLR outcomes, thus improving clinical decision-making and the management of patient expectations. LEVEL OF EVIDENCE Level IV.
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Affiliation(s)
- Bryan Sun
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Prushoth Vivekanantha
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Hassaan Abdel Khalik
- Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - Darren de Sa
- Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
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Sahoo RK, Sahoo KC, Negi S, Baliarsingh SK, Panda B, Pati S. Health professionals' perspectives on the use of Artificial Intelligence in healthcare: A systematic review. PATIENT EDUCATION AND COUNSELING 2025; 134:108680. [PMID: 39893988 DOI: 10.1016/j.pec.2025.108680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 01/09/2025] [Accepted: 01/21/2025] [Indexed: 02/04/2025]
Abstract
INTRODUCTION Artificial Intelligence (AI) is fast emerging as a crucial tool for improving patient care and treatment outcomes; however, concerns persist among health professionals about potential compromises in quality care and loss of jobs. The availability of systematic evidence on health professionals' perspectives on AI in healthcare is limited. OBJECTIVE This systematic review aims to document the perceived advantages and disadvantages associated with AI applications in healthcare. METHOD We conducted a comprehensive search across databases - Embase, PubMed/Medline, IEEE, and Epistemonikos up to November 2023, using 'Artificial Intelligence' AND 'health professionals' as key domains. We searched for studies that describe the perceptions of healthcare professionals towards AI in healthcare. FINDINGS We identified 3931 records. After screening, 25 articles were selected, and 11 were included in the final review. The studies highlight the benefits of AI in healthcare, such as consultation summaries, data management, patient triaging, and referrals, but also raise concerns about job loss, over-reliance, legal implications, and data privacy concerns. CONCLUSION AI enhances care delivery efficiency, and concerns arise due to knowledge and experience gaps. Therefore, healthcare workforce education and skill development are crucial for AI adoption, implementation, and future research.
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Affiliation(s)
- Rakesh Kumar Sahoo
- KIIT School of Public Health, KIIT Deemed to be university, Bhubaneswar - 751024, India; ICMR-Regional Medical Research Centre, Bhubaneswar - 751023, India
| | - Krushna Chandra Sahoo
- Health Technology Assessment in India, Department of Health Research, Ministry of Health & Family Welfare, New Delhi - 11000, India; ICMR-Regional Medical Research Centre, Bhubaneswar - 751023, India
| | - Sapna Negi
- ICMR-Regional Medical Research Centre, Bhubaneswar - 751023, India
| | | | - Bhuputra Panda
- KIIT School of Public Health, KIIT Deemed to be university, Bhubaneswar - 751024, India.
| | - Sanghamitra Pati
- ICMR-Regional Medical Research Centre, Bhubaneswar - 751023, India.
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Miller HA, Valdes R. Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement. Crit Rev Clin Lab Sci 2025:1-20. [PMID: 40247648 DOI: 10.1080/10408363.2025.2488842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2024] [Revised: 01/20/2025] [Accepted: 03/31/2025] [Indexed: 04/19/2025]
Abstract
The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive modeling using clinical laboratory information. Machine learning (ML), a sub-discipline of AI, involves fitting algorithms to datasets and is broadly used for data-driven predictive modeling in various disciplines. The majority of ML studies reported in systematic reviews lack key aspects of quality assurance. In clinical laboratory medicine, it is important to consider how differences in analytical methodologies, assay calibration, harmonization, pre-analytical errors, interferences, and physiological factors affecting measured analyte concentrations may also affect the downstream robustness and reliability of ML models. In this article, we address the need for quality improvement and proper validation of ML classification models, with the goal of bringing attention to key concepts pertinent to researchers, manuscript reviewers, and journal editors within the field of pathology and laboratory medicine. Several existing predictive modeling guidelines and recommendations can be readily adapted to the development of ML models in laboratory medicine. We summarize a basic overview of ML and key points from current guidelines including advantages and pitfalls of applied ML. In addition, we draw a parallel between validation of clinical assays and ML models in the context of current regulatory frameworks. The importance of classification performance metrics, model explainability, and data quality along with recommendations for strengthening journal submission requirements are also discussed. Although the focus of this article is on the application of ML in laboratory medicine, many of these concepts extend into other areas of medicine and biomedical science as well.
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Affiliation(s)
- Hunter A Miller
- Department of Pathology and Laboratory Medicine, University of Louisville, Louisville, KY, USA
| | - Roland Valdes
- Department of Pathology and Laboratory Medicine, University of Louisville, Louisville, KY, USA
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Matharu P, Pertsev E, Chai P, Cheung D, Teng M, Schmidt J, Jarus T. Opinions and Perspectives of Canadian Occupational Therapists on Artificial Intelligence. Can J Occup Ther 2025:84174251327301. [PMID: 40223305 DOI: 10.1177/00084174251327301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2025]
Abstract
Background: Technology is rapidly being developed to improve healthcare outcomes. However, the attitudes and perceptions of occupational therapists (OTs) on artificial intelligence (AI) in healthcare are not yet known. Purpose: This study aims to: explore Canadian OTs' (a) understanding and knowledge on AI, (b) opinions and perspectives on AI, and (c) perceptions of potential benefits and risks AI might bring to occupational therapy practice in Canada. Method: A sequential explanatory mixed method approach was used to gather perspectives of Canadian registered OTs. Two hundred and eighty-two survey respondents and 15 focus group participants took part in the study. Findings: Three main themes emerged: "AI Knowledge and Implementation," "Use of AI in Occupational Therapy," and "Human vs. Machine." OTs have various levels of understanding of AI, and its capabilities within practice and are open to AI use in practice. Although ethical concerns must be addressed, OTs do not perceive AI to pose a threat to employment. Conclusion: OTs have the ability to implement and guide policy changes for technology adoption, and understanding their current perspectives creates opportunities to advocate for change in the field. Further education is needed to better prepare professionals for clinical usage of AI.
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Vyas A, Kumar K, Sharma A, Verma D, Bhatia D, Wahi N, Yadav AK. Advancing the frontier of artificial intelligence on emerging technologies to redefine cancer diagnosis and care. Comput Biol Med 2025; 191:110178. [PMID: 40228444 DOI: 10.1016/j.compbiomed.2025.110178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2025] [Revised: 04/04/2025] [Accepted: 04/07/2025] [Indexed: 04/16/2025]
Abstract
BACKGROUND Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized. METHOD This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions. RESULTS AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges. CONCLUSIONS AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.
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Affiliation(s)
- Akanksha Vyas
- Academy of Scientific and Innovative Research, Ghaziabad, 201002, India
| | - Krishan Kumar
- Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
| | - Ayushi Sharma
- College of Medicine, Taipei Medical University, Taipei City, 110, Taiwan
| | - Damini Verma
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India
| | - Dhiraj Bhatia
- Department of Biological Sciences & Engineering, Indian Institute of Technology Gandhinagar, Near Palaj, Gandhinagar, Gujarat, 382355, India
| | - Nitin Wahi
- Department of Biotechnology, LNCT University, Kolar Road, Shirdipuram, Bhopal, Madhya Pradesh, 462042, India
| | - Amit K Yadav
- Department of Biological Sciences & Engineering, Indian Institute of Technology Gandhinagar, Near Palaj, Gandhinagar, Gujarat, 382355, India.
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Rincón EHH, Jimenez D, Aguilar LAC, Flórez JMP, Tapia ÁER, Peñuela CLJ. Mapping the use of artificial intelligence in medical education: a scoping review. BMC MEDICAL EDUCATION 2025; 25:526. [PMID: 40221725 PMCID: PMC11993958 DOI: 10.1186/s12909-025-07089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Accepted: 04/01/2025] [Indexed: 04/14/2025]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically. OBJECTIVE This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption. MATERIALS AND METHODS A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. RESULTS A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject. CONCLUSIONS This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.
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Affiliation(s)
- Erwin Hernando Hernández Rincón
- Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia.
| | - Daniel Jimenez
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Lizeth Alexandra Chavarro Aguilar
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Juan Miguel Pérez Flórez
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Álvaro Enrique Romero Tapia
- Department of Psychiatry and Mental Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Claudia Liliana Jaimes Peñuela
- Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
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Caviglia M, Argyri KD, Athanasiadis SC, Martignano M, Gui D, Magalini S, Faccincani R, Cioffi S, Tilsed J, O’Mara A, Henriksson L, Tsekeridou S, Lykokanello F, Agarogiannis E, Forcada J, Rampérez V, Antunes N, Rocha da Silva T, Manso M, Guerra B, Laist I, Rafalowski C. Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management. J Med Internet Res 2025; 27:e67318. [PMID: 40209223 DOI: 10.2196/67318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2024] [Revised: 11/30/2024] [Accepted: 12/12/2024] [Indexed: 04/12/2025] Open
Abstract
In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectiveness of AI is heavily reliant on the availability of quality data. Currently, MCI data are scarce and difficult to obtain, as critical information regarding patient demographics, vital signs, and treatment responses is often missing or incomplete, particularly in the prehospital setting. Although the NIGHTINGALE (Novel Integrated Toolkit for Enhanced Pre-Hospital Life Support and Triage in Challenging and Large Emergencies) project is actively addressing these challenges by developing a comprehensive toolkit designed to support first responders and enhance data collection during MCIs, significant work remains to ensure the tools are fully operational and can effectively integrate continuous monitoring and data management. To further advance these efforts, we provide a series of recommendation, advocating for increased European Union funding to facilitate the generation of diverse and high-quality datasets essential for training AI models, including the application of transfer learning and the development of tools supporting data collection during MCIs, while fostering continuous collaboration between end users and technical developers. By securing these resources, we can enhance the efficiency and adaptability of AI applications in emergency care, bridging the current data gaps and ultimately improving outcomes during critical situations.
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Affiliation(s)
- Marta Caviglia
- Center for Research and Training in Disaster Medicine, Humanitarian Aid and Global Health (CRIMEDIM), Università del Piemonte Orientale, Novara, Italy
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Zheng R, Jiang X, Shen L, He T, Ji M, Li X, Yu G. Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey. J Med Internet Res 2025; 27:e62732. [PMID: 40194276 PMCID: PMC12012391 DOI: 10.2196/62732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 03/14/2025] [Accepted: 03/15/2025] [Indexed: 04/09/2025] Open
Abstract
BACKGROUND An intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice. OBJECTIVE The aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intentions to adopt it and by putting forward targeted management recommendations. METHODS This study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of "task-technology fit" and "perceived ease of use" on clinicians' intentions to use the CDSS when mediated by "performance expectation" and "perceived risk." We collated and analyzed the responses to the open-ended question. RESULTS We collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=-0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=-0.281; P<.001) and perceived ease of use (β=-0.377; P<.001) negatively affected perceived risk. Perceived risk (β=-0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians' perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration. CONCLUSIONS Perceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.
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Affiliation(s)
- Rui Zheng
- Shanghai Children's Hospital, Shanghai, China
| | - Xiao Jiang
- School of Public Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Li Shen
- Clinical Research Center, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianrui He
- Zhongshan Hospital Institute of Clinical Science, Fudan University Shanghai Medical College, Shanghai, China
| | - Mengting Ji
- Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xingyi Li
- Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guangjun Yu
- Shanghai Children's Hospital, Shanghai, China
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Ramezani F, Azimi H, Delfanian B, Amanollahi M, Saeidian J, Masoumi A, Farrokhpour H, Khalili Pour E, Khodaparast M. Classification of ocular surface diseases: Deep learning for distinguishing ocular surface squamous neoplasia from pterygium. Graefes Arch Clin Exp Ophthalmol 2025:10.1007/s00417-025-06804-x. [PMID: 40186633 DOI: 10.1007/s00417-025-06804-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2024] [Revised: 02/10/2025] [Accepted: 03/11/2025] [Indexed: 04/07/2025] Open
Abstract
PURPOSE Given the significance and potential risks associated with Ocular Surface Squamous Neoplasia (OSSN) and the importance of its differentiation from other conditions, we aimed to develop a Deep Learning (DL) model differentiating OSSN from pterygium (PTG) using slit photographs. METHODS A dataset comprising slit photographs of 162 patients including 77 images of OSSN and 85 images of PTG was assembled. After manual segmentation of the images, a Python-based transfer learning approach utilizing the EfficientNet B7 network was employed for automated image segmentation. GoogleNet, a pre-trained neural network was used to categorize the images into OSSN or PTG. To evaluate the performance of our DL model, K-Fold 10 Cross Validation was implemented, and various performance metrics were measured. RESULTS There was a statistically significant difference in mean age between the OSSN (63.23 ± 13.74 years) and PTG groups (47.18 ± 11.53) (P-value =.000). Furthermore, 84.41% of patients in the OSSN group and 80.00% of the patients in the PTG group were male. Our classification model, trained on automatically segmented images, demonstrated reliable performance measures in distinguishing OSSN from PTG, with an Area Under Curve (AUC) of 98%, sensitivity, F1 score, and accuracy of 94%, and a Matthews Correlation Coefficient (MCC) of 88%. CONCLUSIONS This study presents a novel DL model that effectively segments and classifies OSSN from PTG images with a relatively high accuracy. In addition to its clinical use, this model can be potentially used as a telemedicine application.
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Affiliation(s)
- Farshid Ramezani
- Clinical Research Development Center, Imam Khomeini, Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Azimi
- Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Avenue, Tehran, Iran
| | - Behrouz Delfanian
- Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Avenue, Tehran, Iran
| | - Mobina Amanollahi
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Saeidian
- Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Avenue, Tehran, Iran
| | - Ahmad Masoumi
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hossein Farrokhpour
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Elias Khalili Pour
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran.
- Retina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, South Kargar Street, Qazvin Square, Qazvin Street, Tehran, Iran.
| | - Mehdi Khodaparast
- Translational Ophthalmology Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
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15
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Clapham E, Picone D, Carmichael S, Bonner C, Chapman N. Appropriateness of Web-Based Resources for Home Blood Pressure Measurement and Their Alignment With Guideline Recommendations, Readability, and End User Involvement: Environmental Scan of Web-Based Resources. JMIR INFODEMIOLOGY 2025; 5:e55248. [PMID: 40179388 PMCID: PMC12006778 DOI: 10.2196/55248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 11/29/2024] [Accepted: 01/11/2025] [Indexed: 04/05/2025]
Abstract
BACKGROUND High blood pressure (≥140/90 mm Hg) is the most prominent mortality risk factor worldwide. Home blood pressure measurement (HBPM) is recommended for blood pressure (BP) management. HBPM is most effective to improve BP management when delivered with patient education. It is unknown whether web-based resources are appropriate for patient education for HBPM. Patient education should provide accurate, evidence-based information, communicate at an eighth grade reading level, and involve end users in development to meet the needs of adults of all health literacy levels. Using these criteria, this study aimed to determine the appropriateness of web-based HBPM resources. OBJECTIVE This study aimed to determine whether web-based resources are appropriate for HBPM education based on three research questions: (1) Do web-based resources provide evidence-based information that aligns with guideline recommendations? (2) Do they communicate at an appropriate reading level? (3) Do they involve end users in their development? METHODS An environmental scan of web-based resources for HBPM was conducted on Google (October 2022) using search terms developed with consumers (n=6). Resources were included if they were identified on the first page of the search findings, not paywalled, and in English. Resource appropriateness was appraised based on three criteria: (1) alignment of resource content to 23 recommendations for HBPM from 6 international guidelines, (2) being at an appropriate grade reading level as determined by a health literacy assessment software, and (3) having evidence of end user involvement in resource development. RESULTS None of the identified resources (n=24) aligned with all 23 of the guideline recommendations. All resources aligned with the recommendation to measure BP when seated, while few aligned with the recommendation to use a validated BP device (n=9, 38%). All resources exceeded the recommended eighth grade reading level (mean 11.8, range 8.8-17.0) and none reported evidence of patient end user involvement in development. CONCLUSIONS None of the web-based resources met the criteria for appropriate education to support adults to measure BP at home. Resources should be developed with end users using health literacy tools and multimodal communication methods to ensure they are appropriate to meet the needs of patients.
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Affiliation(s)
- Eleanor Clapham
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Dean Picone
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Samuel Carmichael
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Carissa Bonner
- Menzies Centre for Health Policy & Economics, School of Public Health, University of Sydney, Sydney, Australia
| | - Niamh Chapman
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
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16
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Larsson I, Svedberg P, Nygren JM, Petersson L. Healthcare leaders' perceptions of the contribution of artificial intelligence to person-centred care: An interview study. Scand J Public Health 2025; 53:72-80. [PMID: 40037338 DOI: 10.1177/14034948241307112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
AIMS The aim of this study was to explore healthcare leaders' perceptions of the contribution of artificial intelligence (AI) to person-centred care (PCC). METHODS The study had an explorative qualitative approach. Individual interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders in a county council in Sweden. An abductive qualitative content analysis was conducted based on McCormack and McCance's framework of PCC. The four constructs (i.e. prerequisites, care environment, person-centred processes and expected outcomes) constituted the four categories for the deductive analysis. The inductive analysis generated 11 subcategories to the four constructs, representing how AI could contribute to PCC. RESULTS Healthcare leaders perceived that AI applications could contribute to the four PCC constructs through (a) supporting professional competence and establishing trust among healthcare professionals and patients (prerequisites); (b) including AI's ability to facilitate patient safety, enable proactive care, provide treatment recommendations and prioritise healthcare resources (the care environment); (c) including AI's ability to tailor information and promote the process of shared decision making and self-management (person-centred processes); and (d) including improving care quality and promoting health outcomes (expected outcomes). CONCLUSIONS The healthcare leaders perceived that AI applications could contribute to PCC at different levels of healthcare, thereby enhancing the quality of care and patients' health.
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Affiliation(s)
- Ingrid Larsson
- School of Health and Welfare, Halmstad University, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Sweden
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Sweden
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17
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Pinero de Plaza MA, Lambrakis K, Marmolejo-Ramos F, Beleigoli A, Archibald M, Yadav L, McMillan P, Clark R, Lawless M, Morton E, Hendriks J, Kitson A, Visvanathan R, Chew DP, Barrera Causil CJ. Human-centred AI for emergency cardiac care: Evaluating RAPIDx AI with PROLIFERATE_AI. Int J Med Inform 2025; 196:105810. [PMID: 39893766 DOI: 10.1016/j.ijmedinf.2025.105810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2024] [Revised: 01/21/2025] [Accepted: 01/23/2025] [Indexed: 02/04/2025]
Abstract
BACKGROUND Chest pain diagnosis in emergency care is hindered by overlapping cardiac and non-cardiac symptoms, causing diagnostic uncertainty. Artificial Intelligence, such as RAPIDx AI, aims to enhance accuracy through clinical and biochemical data integration, but its adoption relies on addressing usability, explainability, and seamless workflow integration without disrupting care. OBJECTIVE Evaluate RAPIDx AI's integration into clinical workflows, address usability barriers, and optimise its adoption in emergencies. METHODS The PROLIFERATE_AI framework was implemented across 12 EDs (July 2022-January 2024) with 39 participants: 15 experts co-designed a survey via Expert Knowledge Elicitation (EKE), applied to 24 ED clinicians to assess RAPIDx AI usability and adoption. Bayesian inference, using priors, estimated comprehension, emotional engagement, usage, and preference, while Monte Carlo simulations quantified uncertainty and variability, generating posterior means and 95% bootstrapped confidence intervals. Qualitative thematic analysis identified barriers and optimisation needs, with data triangulated through the PROLIFERATE_AI scoring system to rate RAPIDx AI's performance by user roles and demographics. RESULTS Registrars exhibited the highest comprehension (median: 0.466, 95 % CI: 0.41-0.51) and preference (median: 0.458, 95 % CI: 0.41-0.48), while residents/interns scored the lowest in comprehension (median: 0.198, 95 % CI: 0.17-0.26) and emotional engagement (median: 0.112, 95 % CI: 0.09-0.14). Registered nurses showed strong emotional engagement (median: 0.379, 95 % CI: 0.35-0.45). Novice users faced usability and workflow integration barriers, while experienced clinicians suggested automation and streamlined workflows. RAPIDx AI scored "Good Impact," excelling with trained users but requiring targeted refinements for novices. CONCLUSION RAPIDx AI enhances diagnostic accuracy and efficiency for experienced users, but usability challenges for novices highlight the need for targeted training and interface refinements. The PROLIFERATE_AI framework offers a robust methodology for evaluating and scaling AI solutions, addressing the evolving needs of sociotechnical systems.
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Affiliation(s)
| | - Kristina Lambrakis
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia; MonashHeart, Monash Health, Melbourne, Victoria, Australia; College of Medicine and Public Health, Flinders University, Adelaide, South, Australia
| | | | - Alline Beleigoli
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Mandy Archibald
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Lalit Yadav
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Penelope McMillan
- South Australian Health and Medical Research Institute (SAHMRI), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) Collaborative, Adelaide, South, Australia
| | - Robyn Clark
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Michael Lawless
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Erin Morton
- Bespoke Clinical Research, Adelaide, South, Australia
| | - Jeroen Hendriks
- Department of Nursing, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Alison Kitson
- Caring Futures Institute, Flinders University, Adelaide, South, Australia
| | - Renuka Visvanathan
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, South, Australia
| | - Derek P Chew
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia; MonashHeart, Monash Health, Melbourne, Victoria, Australia; College of Medicine and Public Health, Flinders University, Adelaide, South, Australia
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18
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Dafni MF, Shih M, Manoel AZ, Yousif MYE, Spathi S, Harshal C, Bhatt G, Chodnekar SY, Chune NS, Rasool W, Umar TP, Moustakas DC, Achkar R, Kumar H, Naz S, Acuña-Chavez LM, Evgenikos K, Gulraiz S, Ali ESM, Elaagib A, Uggh IHP. Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention. Cancer Causes Control 2025; 36:353-367. [PMID: 39672997 DOI: 10.1007/s10552-024-01942-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Accepted: 11/18/2024] [Indexed: 12/15/2024]
Abstract
Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.
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Affiliation(s)
- Marianna-Foteini Dafni
- School of Medicine, Laboratory of Forensic Medicine and Toxicology, Aristotle Univerisity of Thessaloniki, Thessaloniki, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Shih
- School of Medicine, Newgiza University, Giza, Egypt.
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece.
| | - Agnes Zanotto Manoel
- Faculty of Medicine, Federal University of Rio Grande, Rio Grande do Sul, Brazil
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Yousif Elamin Yousif
- Faculty of Medicine, University of Khartoum, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Stavroula Spathi
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Chorya Harshal
- Faculty of Medicine, Medical College Baroda, Vadodara, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Gaurang Bhatt
- All India Institute of Medical Sciences, Rishikesh, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Swarali Yatin Chodnekar
- Faculty of Medicine, Teaching University Geomedi LLC, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Nicholas Stam Chune
- Faculty of Medicine, University of Nairobi, Nairobi, Kenya
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Warda Rasool
- Faculty of Medicine, King Edward Medical University, Lahore, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Tungki Pratama Umar
- Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Dimitrios C Moustakas
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Robert Achkar
- Faculty of Medicine, Poznan University of Medical Sciences, Poznan, Poland
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Harendra Kumar
- Dow University of Health Sciences, Karachi, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Suhaila Naz
- Tbilisi State Medical University, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Luis M Acuña-Chavez
- Facultad de Medicina de la Universidad Nacional de Trujillo, Trujillo, Peru
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Konstantinos Evgenikos
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Shaina Gulraiz
- Royal Bournemouth Hospital (University Hospitals Dorset), Bournemouth, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Eslam Salih Musa Ali
- University of Dongola Faculty of Medicine and Health Science, Dongola, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Amna Elaagib
- Faculty of Medicine AlMughtaribeen University, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Innocent H Peter Uggh
- Kilimanjaro Clinical Research Institute, Kilimanjaro, Tanzania
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
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Smith ME, Zalesky CC, Lee S, Gottlieb M, Adhikari S, Goebel M, Wegman M, Garg N, Lam SH. Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert. J Am Coll Emerg Physicians Open 2025; 6:100051. [PMID: 40034198 PMCID: PMC11874537 DOI: 10.1016/j.acepjo.2025.100051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/15/2024] [Accepted: 01/02/2025] [Indexed: 03/05/2025] Open
Abstract
Artificial intelligence (AI) is increasingly being utilized to augment the practice of emergency medicine due to rapid technological advances and breakthroughs. AI applications have been used to enhance triage systems, predict disease-specific risk, estimate staffing needs, forecast patient decompensation, and interpret imaging findings in the emergency department setting. This article aims to help readers without formal training become informed end-users of AI in emergency medicine. The authors will briefly discuss the principles and key terminology of AI, the reasons for its rising popularity, its potential applications in the emergency department setting, and its limitations. Additionally, resources for further self-studying will also be provided.
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Affiliation(s)
- Moira E. Smith
- Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - C. Christopher Zalesky
- Department of Anesthesia, Division of Critical Care, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Sangil Lee
- Department of Emergency Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Michael Gottlieb
- Emergency Ultrasound Division, Department of Emergency Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Srikar Adhikari
- Department of Emergency Medicine, University of Arizona, Tucson, Arizona, USA
| | - Mat Goebel
- Department of Emergency Medicine, Mercy Medical Center - Trinity Health of New England, Springfield, Massachusetts, USA
| | - Martin Wegman
- Department of Emergency Medicine, Orange Park Medical Center, Orange Park, Florida, USA
| | - Nidhi Garg
- Department of Emergency Medicine, South Shore University Hospital/Northwell Health, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
| | - Samuel H.F. Lam
- Section of Emergency Medicine, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, USA
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20
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Liu J, Segal K, Daher M, Ozolin J, Binder WD, Bergen M, McDonald CL, Owens BD, Antoci V. Artificial intelligence versus orthopedic surgeons as an orthopedic consultant in the emergency department. Injury 2025; 56:112297. [PMID: 40147063 DOI: 10.1016/j.injury.2025.112297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2024] [Revised: 03/16/2025] [Accepted: 03/20/2025] [Indexed: 03/29/2025]
Abstract
INTRODUCTION ChatGPT, a widely accessible AI program, has demonstrated potential in various healthcare applications, including emergency department (ED) triage, differential diagnosis, and patient education. However, its potential in providing recommendations to emergency department providers with orthopedic consultations has not been evaluated yet. METHODS This study compared the performance of four board certified orthopedic surgeons, two attendings and two trauma fellows who take independent call at the same institution and ChatGPT-4 in responding to clinical scenarios commonly encountered in emergency departments. Five common orthopedic ED scenarios were developed (lateral malleolar ankle fractures, distal radius fractures, septic arthritis of the knee, shoulder dislocations, and Achilles tendon ruptures), each with four questions related to diagnosis, management, surgical indication, and patient counseling, totaling 20 questions. Responses were anonymized, coded, and evaluated by independent reviewers including emergency medicine physicians using a five-point Likert scale across five criteria: accuracy, completeness, helpfulness, specificity, and overall quality. RESULTS When comparing the ratings of AI answers to non-AI responders, the AI answers were shown to be superior in completeness, helpfulness, specificity, and overall quality with no difference in regards to accuracy (p < 0.05). When considering question subtypes including diagnosis, management, treatment, and patient counseling, AI was shown to have superior scores in helpfulness, and specificity in diagnostic questions(p < 0.05). In addition, AI responses were superior in all the assessed categories when looking at the patient counseling questions (p < 0.05). When considering different clinical scenarios, AI outperformed non-AI groups in completeness in the distal radius fracture scenario. Furthermore, AI outperformed non-AI groups in helpfulness in the lateral malleolus fracture scenario. In the shoulder dislocation scenario, AI responses were more complete, helpful, and had a better overall quality. AI responses were non-inferior in the remaining categories of the different scenarios. CONCLUSION Artificial intelligence exhibited non-inferior and often superior performance in common orthopedic-ED consultations compared to board certified orthopedic surgeons While current AI models are limited in their ability to integrate specific images and patient scenarios, our findings suggest AI can provide high quality recommendations for generic orthopedic consultations and with further development, will likely have an increasing role in the future.
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Affiliation(s)
- Jonathan Liu
- Department of Orthopedics, Brown University, Providence, RI, USA
| | - Kathryn Segal
- Department of Orthopedics, Brown University, Providence, RI, USA
| | - Mohammad Daher
- Department of Orthopedics, Brown University, Providence, RI, USA
| | - Jordan Ozolin
- Department of Orthopedics, Brown University, Providence, RI, USA
| | - William D Binder
- Department of Emergency Medicine, Brown University, Providence, RI, USA
| | - Michael Bergen
- Department of Orthopedics, Brown University, Providence, RI, USA
| | | | - Brett D Owens
- Department of Orthopedics, Brown University, Providence, RI, USA
| | - Valentin Antoci
- Department of Orthopedics, Brown University, Providence, RI, USA.
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Clay B, Bergman HI, Salim S, Pergola G, Shalhoub J, Davies AH. Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies. Comput Biol Med 2025; 188:109808. [PMID: 39946783 DOI: 10.1016/j.compbiomed.2025.109808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 02/03/2025] [Accepted: 02/04/2025] [Indexed: 03/05/2025]
Abstract
Natural Language Processing (NLP) has the potential to revolutionise clinical research utilising Electronic Health Records (EHR) through the automated analysis of unstructured free text. Despite this potential, relatively few applications have entered real-world clinical practice. This paper aims to introduce the whole pipeline of NLP methodologies for EHR analysis to the clinical researcher, with case studies to demonstrate the application of these methods in the existing literature. Essential pre-processing steps are introduced, followed by the two major classes of analytical frameworks: statistical methods and Artificial Neural Networks (ANNs). Case studies which apply statistical and ANN-based methods are then provided and discussed, illustrating information extraction tasks for objective and subjective information, and classification/prediction tasks using supervised and unsupervised approaches. State-of-the-art large language models and future directions for research are then discussed. This educational article aims to bridge the gap between the clinical researcher and the NLP expert, providing clinicians with a background understanding of the NLP techniques relevant to EHR analysis, allowing engagement with this rapidly evolving area of research, which is likely to have a major impact on clinical practice in coming years.
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Affiliation(s)
- Benjamin Clay
- Department of Trauma and Orthopaedic Surgery, East Suffolk and North Essex NHS Foundation Trust, Ipswich Hospital, Heath Road, Ipswich, IP4 5PD, United Kingdom; Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, United Kingdom.
| | - Henry I Bergman
- Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, United Kingdom.
| | - Safa Salim
- Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, United Kingdom.
| | - Gabriele Pergola
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Joseph Shalhoub
- Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, United Kingdom.
| | - Alun H Davies
- Academic Section of Vascular Surgery, Department of Surgery and Cancer, Imperial College London, London, SW7 2AZ, United Kingdom.
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McGrow K. Artificial intelligence in nursing: A journey from data to wisdom. Nursing 2025; 55:16-24. [PMID: 40122866 PMCID: PMC11922186 DOI: 10.1097/nsg.0000000000000165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2025]
Abstract
ABSTRACT Artificial intelligence (AI) can enhance nursing practice by assisting in clinical decisions, patient outcomes, and operational efficiencies. This article explores the role of AI in decision-making, data management, and task automation within the Data, Information, Knowledge, Wisdom Framework. It also addresses data quality, ethical considerations, and the need for continuous AI system improvement, emphasizing AI as a valuable healthcare partner.
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Affiliation(s)
- Kathleen McGrow
- Kathleen McGrow is the Global Chief Nursing Innovation Officer at Microsoft Health & Life Sciences in Redmond, Wash., and an adjunct clinical instructor at The University of Alabama at Birmingham School of Nursing
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Sarikaya M, Ozcan Siki F, Ciftci I. Use of Artificial Intelligence in Vesicoureteral Reflux Disease: A Comparative Study of Guideline Compliance. J Clin Med 2025; 14:2378. [PMID: 40217829 PMCID: PMC11989457 DOI: 10.3390/jcm14072378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2025] [Revised: 03/26/2025] [Accepted: 03/28/2025] [Indexed: 04/14/2025] Open
Abstract
Objective: This study aimed to evaluate the compliance of four different artificial intelligence applications (ChatGPT-4.0, Bing AI, Google Bard, and Perplexity) with the American Urological Association (AUA) vesicoureteral reflux (VUR) management guidelines. Materials and Methods: Fifty-one questions derived from the AUA guidelines were asked of each AI application. Two experienced paediatric surgeons independently scored the responses using a five-point Likert scale. Inter-rater agreement was analysed using the intraclass correlation coefficient (ICC). Results: ChatGPT-4.0, Bing AI, Google Bard, and Perplexity received mean scores of 4.91, 4.85, 4.75 and 4.70 respectively. There was no statistically significant difference between the accuracy of the AI applications (p = 0.223). The inter-rater ICC values were above 0.9 for all platforms, indicating a high level of consistency in scoring. Conclusions: The evaluated AI applications agreed highly with the AUA VUR management guidelines. These results suggest that AI applications may be a potential tool for providing guideline-based recommendations in paediatric urology.
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Affiliation(s)
- Mehmet Sarikaya
- Department of Pediatric Surgery, Faculty of Medicine, Selcuk University, Konya 42100, Turkey; (F.O.S.); (I.C.)
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Chen D, Alnassar SA, Avison KE, Huang RS, Raman S. Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review. JMIR Cancer 2025; 11:e65984. [PMID: 40153782 PMCID: PMC11970800 DOI: 10.2196/65984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2024] [Revised: 01/23/2025] [Accepted: 01/27/2025] [Indexed: 03/30/2025] Open
Abstract
Background Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care. Objective This scoping review aims to map the current landscape, including definitions, frameworks, and future directions of LLMs applied to data extraction from clinical text in oncology. Methods We queried Ovid MEDLINE for primary, peer-reviewed research studies published since 2000 on June 2, 2024, using oncology- and LLM-related keywords. This scoping review included studies that evaluated the performance of an LLM applied to data extraction from clinical text in oncology contexts. Study attributes and main outcomes were extracted to outline key trends of research in LLM-based data extraction. Results The literature search yielded 24 studies for inclusion. The majority of studies assessed original and fine-tuned variants of the BERT LLM (n=18, 75%) followed by the Chat-GPT conversational LLM (n=6, 25%). LLMs for data extraction were commonly applied in pan-cancer clinical settings (n=11, 46%), followed by breast (n=4, 17%), and lung (n=4, 17%) cancer contexts, and were evaluated using multi-institution datasets (n=18, 75%). Comparing the studies published in 2022-2024 versus 2019-2021, both the total number of studies (18 vs 6) and the proportion of studies using prompt engineering increased (5/18, 28% vs 0/6, 0%), while the proportion using fine-tuning decreased (8/18, 44.4% vs 6/6, 100%). Advantages of LLMs included positive data extraction performance and reduced manual workload. Conclusions LLMs applied to data extraction in oncology can serve as useful automated tools to reduce the administrative burden of reviewing patient health records and increase time for patient-facing care. Recent advances in prompt-engineering and fine-tuning methods, and multimodal data extraction present promising directions for future research. Further studies are needed to evaluate the performance of LLM-enabled data extraction in clinical domains beyond the training dataset and to assess the scope and integration of LLMs into real-world clinical environments.
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Affiliation(s)
- David Chen
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Saif Addeen Alnassar
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Kate Elizabeth Avison
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Ryan S Huang
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Srinivas Raman
- Department of Radiation Oncology, BC Cancer Vancouver, 600 W 10th Ave, Vancouver, BC, V5Z 4E6, Canada, 1 416-946-4501
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Kula B, Kula A, Bagcier F, Alyanak B. Artificial intelligence solutions for temporomandibular joint disorders: Contributions and future potential of ChatGPT. Korean J Orthod 2025; 55:131-141. [PMID: 40104855 PMCID: PMC11922634 DOI: 10.4041/kjod24.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 10/25/2024] [Accepted: 12/09/2024] [Indexed: 03/20/2025] Open
Abstract
Objective This study aimed to evaluate the reliability and usefulness of information generated by Chat Generative Pre-Trained Transformer (ChatGPT) on temporomandibular joint disorders (TMD). Methods We asked ChatGPT about the diseases specified in the TMD classification and scored the responses using Likert reliability and usefulness scales, the modified DISCERN (mDISCERN) scale, and the Global Quality Scale (GQS). Results The highest Likert scores for both reliability and usefulness were for masticatory muscle disorders (mean ± standard deviation [SD]: 6.0 ± 0), and the lowest scores were for inflammatory disorders of the temporomandibular joint (mean ± SD: 4.3 ± 0.6 for reliability, 4.0 ± 0 for usefulness). The median Likert reliability score indicates that the responses are highly reliable. The median Likert usefulness score was 5 (4-6), indicating that the responses were moderately useful. A comparative analysis was performed, and no statistically significant differences were found in any subject for either reliability or usefulness (P = 0.083-1.000). The median mDISCERN score was 4 (3-5) for the two raters. A statistically significant difference was observed in the mean mDISCERN scores between the two raters (P = 0.046). The GQS scores indicated a moderate to high quality (mean ± SD: 3.8 ± 0.8 for rater 1, 4.0 ± 0.5 for rater 2). No statistically significant correlation was found between mDISCERN and GQS scores (r = -0.006, P = 0.980). Conclusions Although ChatGPT-4 has significant potential, it can be used as an additional source of information regarding TMD for patients and clinicians.
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Affiliation(s)
- Betul Kula
- Department of Orthodontics, Istanbul Galata University, Istanbul, Türkiye
| | - Ahmet Kula
- Department of Prosthodontics, Uskudar University, Istanbul, Türkiye
| | - Fatih Bagcier
- Physical Medicine and Rehabilitation Clinic, Basaksehir Cam and Sakura City Hospital, Istanbul, Türkiye
| | - Bulent Alyanak
- Department of Physical Medicine and Rehabilitation, Golcuk Necati Celik State Hospital, Kocaeli, Türkiye
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Aravazhi PS, Gunasekaran P, Benjamin NZY, Thai A, Chandrasekar KK, Kolanu ND, Prajjwal P, Tekuru Y, Brito LV, Inban P. The integration of artificial intelligence into clinical medicine: Trends, challenges, and future directions. Dis Mon 2025:101882. [PMID: 40140300 DOI: 10.1016/j.disamonth.2025.101882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2025]
Abstract
BACKGROUND AND OBJECTIVES AI has emerged as a transformative force in clinical medicine, changing the diagnosis, treatment, and management of patients. Tools have been derived for working with ML, DL, and NLP algorithms to analyze large complex medical datasets with unprecedented accuracy and speed, thereby improving diagnostic precision, treatment personalization, and patient care outcomes. For example, CNNs have dramatically improved the accuracy of medical imaging diagnoses, and NLP algorithms have greatly helped extract insights from unstructured data, including EHRs. However, there are still numerous challenges that face AI integration into clinical workflows, including data privacy, algorithmic bias, ethical dilemmas, and problems with the interpretability of "black-box" AI models. These barriers have thus far prevented the widespread application of AI in health care, and its possible trends, obstacles, and future implications are necessary to be systematically explored. The purpose of this paper is, therefore, to assess the current trends in AI applications in clinical medicine, identify those obstacles that are hindering adoption, and identify possible future directions. This research hopes to synthesize evidence from other peer-reviewed articles to provide a more comprehensive understanding of the role that AI plays to advance clinical practices, improve patient outcomes, or enhance decision-making. METHODS A systematic review was done according to the PRISMA guidelines to explore the integration of Artificial Intelligence in clinical medicine, including trends, challenges, and future directions. PubMed, Cochrane Library, Web of Science, and Scopus databases were searched for peer-reviewed articles from 2014 to 2024 with keywords such as "Artificial Intelligence in Medicine," "AI in Clinical Practice," "Machine Learning in Healthcare," and "Ethical Implications of AI in Medicine." Studies focusing on AI application in diagnostics, treatment planning, and patient care reporting measurable clinical outcomes were included. Non-clinical AI applications and articles published before 2014 were excluded. Selected studies were screened for relevance, and then their quality was critically appraised to synthesize data reliably and rigorously. RESULTS This systematic review includes the findings of 8 studies that pointed out the transformational role of AI in clinical medicine. AI tools, such as CNNs, had diagnostic accuracy more than the traditional methods, particularly in radiology and pathology. Predictive models efficiently supported risk stratification, early disease detection, and personalized medicine. Despite these improvements, significant hurdles, including data privacy, algorithmic bias, and resistance from clinicians regarding the "black-box" nature of AI, had yet to be surmounted. XAI has emerged as an attractive solution that offers the promise to enhance interpretability and trust. As a whole, AI appeared promising in enhancing diagnostics, treatment personalization, and clinical workflows by dealing with systemic inefficiencies. CONCLUSION The transformation potential of AI in clinical medicine can transform diagnostics, treatment strategies, and efficiency. Overcoming obstacles such as concerns about data privacy, the danger of algorithmic bias, and difficulties with interpretability may pave the way for broader use and facilitate improvement in patient outcomes while transforming clinical workflows to bring sustainability into healthcare delivery.
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Affiliation(s)
| | | | | | - Andy Thai
- Internal Medicine, Alameda Health System, Highland Hospital, Oakland, USA
| | | | | | | | - Yogesh Tekuru
- RVM Institute of Medical Sciences and Research Center, Laxmakkapally, India
| | | | - Pugazhendi Inban
- Internal Medicine, St. Mary's General Hospital and Saint Clare's Health, NY, USA.
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Raghareutai K, Tanchotsrinon W, Sattayalertyanyong O, Kaosombatwattana U. Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding. BMC Med Inform Decis Mak 2025; 25:145. [PMID: 40128792 PMCID: PMC11934503 DOI: 10.1186/s12911-025-02969-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Accepted: 03/12/2025] [Indexed: 03/26/2025] Open
Abstract
BACKGROUND Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, endoscopic intervention is the mainstay treatment for hemostasis in high-risk rebleeding lesions. Predicting the need for endoscopic intervention would be beneficial in resource-limited areas for selective referral to an endoscopic center. The proposed risk stratification scores had limited accuracy. We developed a machine learning model to predict the need for endoscopic intervention in patients with acute UGIB. METHODS A prospectively collected database of UGIB patients from 2011 to 2020 was retrospectively reviewed. Patients older than 18 years diagnosed with UGIB who underwent endoscopy were included. Data comprised demographic characteristics, clinical presentation, and laboratory parameters. The cleaned data was used for model development and validation in Python. We conducted 80%-20% split sample training and test sets. The training set was used for supervised learning of 15 models using a stratified 5-fold cross-validation process. The model with the highest AUROC was then internally validated with the test set to evaluate performance. RESULTS Of 1389 patients, 615 (44.3%) of the cohorts received the endoscopic intervention (293 variceal- and 336 nonvariceal-bleeding interventions). Eighteen features, including demographic characteristics, clinical presentation, and laboratory parameters, were selected as input for 15 machine learning models. The result revealed that the linear discriminant analysis model could achieve the highest AUROC of 0.74 to predict endoscopic intervention. The model was validated with the test set, in which the AUROC was increased from 0.74 to 0.81. Finally, the model was deployed as a web application by Streamlit. CONCLUSIONS Our machine learning model can identify patients with acute UGIB who need endoscopic intervention with good performance. This may help primary care physicians prioritize patients who need referrals and optimize resource allocation in resource-limited areas. Further development and identification of more specific features might improve prediction performance. TRIAL REGISTRATION None (Retrospective cohort study) PATIENT & PUBLIC INVOLVEMENT: None.
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Affiliation(s)
- Kajornvit Raghareutai
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | | | | | - Uayporn Kaosombatwattana
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand.
- Siriraj GI endoscopy Center, Siriraj Hospital, Bangkok, Thailand.
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Nishibe T, Iwasa T, Kano M, Akiyama S, Fukuda S, Koizumi J, Nishibe M. Predicting Long-Term Survival after Endovascular Aneurysm Repair Using Machine Learning-Based Decision Tree Analysis. Vasc Endovascular Surg 2025:15385744251329673. [PMID: 40123361 DOI: 10.1177/15385744251329673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2025]
Abstract
ObjectiveEndovascular aneurysm repair (EVAR) has become a preferred method for treating abdominal aortic aneurysms (AAA) due to its minimally invasive approach. However, identifying factors that influence long-term patient outcomes is crucial for improving prognosis. This study investigates whether machine learning (ML)-based decision tree analysis (DTA) can predict long-term survival (over 5 years postoperatively) by uncovering complex patterns in patient data.MethodsWe retrospectively analyzed data from 142 patients who underwent elective EVAR for AAA at Tokyo Medical University Hospital between October 2013 and July 2018. The dataset comprised 24 variables, including age, gender, nutritional status, comorbidities, and surgical details. The decision tree classifier was developed and validated using Python 3.7 and the scikit-learn toolkit.ResultsDTA identified poor nutritional status as the most significant predictor, followed by compromised immunity, active cancer, octogenarians, chronic kidney disease, and chronic obstructive pulmonary disease. The decision tree identified 9 terminal nodes with probabilities of long-term survival. Four of these terminal nodes represented groups of patients with a high probability of long-term survival: 100%, 84%, 77%, and 60%, whereas the other 5 terminal nodes represented groups of patients with a low probability of long-term survival: 17%, 25%, 30%, 45%, and 47%. The model achieved a moderately high accuracy of 76.1%, specificity of 72.4%, sensitivity of 81.8%, precision of 65.2%, and area under the receiver operating characteristic curve of 0.84.ConclusionML-based DTA effectively predicts long-term survival after EVAR, highlighting the importance of comprehensive preoperative assessments and personalized management strategies to improve patient outcomes.
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Affiliation(s)
- Toshiya Nishibe
- Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan
- Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan
| | - Tsuyoshi Iwasa
- Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan
| | - Masaki Kano
- Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan
| | - Shinobu Akiyama
- Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan
| | - Shoji Fukuda
- Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan
| | - Jun Koizumi
- Department of Radiology, Chiba University School of Medicine, Chiba, Japan
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Gonzalez Aroca J, Vergara-Merino L, Escobar Liquitay CM, Farías H, Arancibia JO, Puelles Á, Madrid E. Role of artificial intelligence-powered conversational agents (chatbots) in musculoskeletal disorders: a scoping review protocol. BMJ Open 2025; 15:e092982. [PMID: 40132840 PMCID: PMC11934414 DOI: 10.1136/bmjopen-2024-092982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Accepted: 02/03/2025] [Indexed: 03/27/2025] Open
Abstract
INTRODUCTION Musculoskeletal disorders (MSDs) represent a significant global health burden that leads to substantial disability with socioeconomic impact. With the rise of artificial intelligence (AI), particularly large language model-driven conversational agents (chatbots), there is potential to enhance the management of MSDs. However, the application of AI-powered chatbots in this population has not been comprehensively synthesised. Therefore, this scoping review aims to explore the current and potential use of AI-powered chatbots in managing MSDs. The review will map out the targeted diseases, the purposes of chatbot interventions, the clinical tools or frameworks used in training these systems and the evaluated outcomes in clinical settings. METHODS AND ANALYSIS This scoping review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, with a comprehensive search across multiple databases, including Medline (Ovid Medline), Embase (Ovid), ISI Web of Science (Clarivate) and ClinicalTrials.gov. We will include studies involving adults with MSDs, regardless of publication status, language or year. The scoping review will exclude studies using non-AI chatbots or human health coaches. Data extraction and synthesis will focus on demographic characteristics, chatbot methods, outcomes and thematic analysis. ETHICS AND DISSEMINATION Formal ethical approval is not required as this study involves neither human participants nor unpublished secondary data. The findings of this scoping review will be disseminated through professional networks, conference presentations and publication in a scientific journal.
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Affiliation(s)
| | - Laura Vergara-Merino
- Department of Traumatology and Orthopedics, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Humberto Farías
- Department of Industrial Engineering, Universidad de La Serena, La Serena, Chile
| | | | | | - Eva Madrid
- Escuela de Medicina, Universidad de Valparaiso, Valparaiso, Chile
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Macrì D, Ramacciati N, Comito C, Metlichin E, Giusti GD, Forestiero A. Enhancing Chronic Pain Nursing Diagnosis Through Machine Learning: A Performance Evaluation. Comput Inform Nurs 2025:00024665-990000000-00304. [PMID: 40111146 DOI: 10.1097/cin.0000000000001277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2025]
Abstract
This study proposes an evaluation of the efficacy of machine learning algorithms in classifying chronic pain based on Italian nursing notes, contributing to the integration of artificial intelligence tools in healthcare within an Italian linguistic context. The research aimed to validate the nursing diagnosis of chronic pain and explore the potential of artificial intelligence (AI) in enhancing clinical decision-making in Italian healthcare settings. Three machine learning algorithms-XGBoost, gradient boosting, and BERT-were optimized through a grid search approach to identify the most suitable hyperparameters for each model. Therefore, the performance of the algorithms was evaluated and compared using Cohen's κ coefficient. This statistical measure assesses the level of agreement between the predicted classifications and the actual data labels. Results demonstrated XGBoost's superior performance, whereas BERT showed potential in handling complex Italian language structures despite data volume and domain specificity limitations. The study highlights the importance of algorithm selection in clinical applications and the potential of machine learning in healthcare, specifically addressing the challenges of Italian medical language processing. This work contributes to the growing field of artificial intelligence in nursing, offering insights into the challenges and opportunities of implementing machine learning in Italian clinical practice. Future research could explore integrating multimodal data, combining text analysis with physiological signals and imaging data, to create more comprehensive and accurate chronic pain classification models tailored to the Italian healthcare system.
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Affiliation(s)
- Davide Macrì
- Author Affiliations: Istituto di Calcolo e Reti ad Alte Prestazioni (Institute for High-Performance Computing and Networking) (Drs Macrì, Comito, and Forestiero); and Department of Pharmacy, Health and Nutritional Sciences, Università della Calabria (Dr Ramacciati), Rende, Cosenza; Residenze Protette Cerreto d'Esi (Residential Care Facility), Kursana lunga vita Coop. Soc. ONLUS, Cerreto d'Esi, Ancona (Dr Metlichin); and Nursing School, University of Perugia (Dr Giusti); and Servizio Formazione e Qualità, Azienda Ospedaliera di Perugia (Dr Giusti), Perugia, Italy
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Heffernan A, Ganguli R, Sears I, Stephen AH, Heffernan DS. Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients. Surg Infect (Larchmt) 2025. [PMID: 40107772 DOI: 10.1089/sur.2024.288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2025] Open
Abstract
Background: Surgical quality datasets are critical to decision-making tools including surgical infection (SI). Machine learning models (MLMs), a branch of artificial intelligence (AI), are increasingly being ingrained within surgical decision-making algorithms. However, given the unique and distinct functioning of individual models, not all models may be suitable for acutely ill surgical patients. Patients and Methods: This is a 5-year retrospective review of National Surgical Quality Improvement Program (NSQIP) patients who underwent an operation. The data were reviewed for demographics, medical comorbidities, rates, and sites of infection. To generate the MLMs, data were imported into Python, and four common MLMs, extreme gradient boosting, K-nearest neighbor (KNN), random forest, and logistic regression, as well as two novel models (flexible discriminant analysis and generalized additive model) and ensemble modeling, were generated to predict post-operative SIs. Outputs included area under the receiver-operating characteristic curve (AUC ROC) including recall curves. Results: Overall, 624,625 urgent and emergent NSQIP patients were included. The overall infection rate was 8.6%. Patients who sustained a post-operative infection were older, more likely geriatric, male, diabetic, had chronic obstructive pulmonary disease, were smokers, and were less likely White race. With respect to MLMs, all four MLMs had reasonable accuracy. However, a hierarchy of MLMs was noted with predictive abilities (XGB AUC = 0.85 and logistic regression = 0.82), wherein KNN has the lowest performance (AUC = 0.62). With respect to the ability to detect an infection, precision recall of XGB performed well (AUC = 0.73), whereas KNN performed poorly (AUC = 0.16). Conclusions: MLMs are not created nor function similarly. We identified differences with MLMs to predict post-operative infections in surgical patients. Before MLMs are incorporated into surgical decision making, it is critical that surgeons are at the fore of understanding the role and functioning of MLMs.
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Affiliation(s)
- Addison Heffernan
- Division of Trauma and Surgical Critical Care, Department of Surgery, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA
| | - Reetam Ganguli
- Division of Trauma and Surgical Critical Care, Department of Surgery, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA
| | - Isaac Sears
- Division of Trauma and Surgical Critical Care, Department of Surgery, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA
| | - Andrew H Stephen
- Division of Trauma and Surgical Critical Care, Department of Surgery, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA
| | - Daithi S Heffernan
- Division of Trauma and Surgical Critical Care, Department of Surgery, Warren Alpert School of Medicine, Brown University, Providence, Rhode Island, USA
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Ofem UJ, Anake PM, Abuo CB, Ukatu JO, Etta EO. Artificial intelligence application in counselling practices. A multigroup analysis of acceptance and awareness using gender and professional rank. Front Digit Health 2025; 6:1414178. [PMID: 40176970 PMCID: PMC11962729 DOI: 10.3389/fdgth.2024.1414178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 12/23/2024] [Indexed: 04/05/2025] Open
Abstract
Introduction Artificial intelligence (AI) has emerged as a transformative tool in various professional domains, including counselling, where it offers innovative ways to enhance service delivery and client outcomes. Despite its potential, research on AI in counselling practices often focuses on its technical applications, with limited attention to the interplay between awareness, acceptance, and application. This study analyses how professional counsellors apply artificial intelligence in counselling practices using the nexus between awareness and application through acceptance of AI with gender and professional rank as group. Method A total of 5,432 professional counsellors were selected for the study. Data collection was conducted online to ensure a wide reach. The research instruments underwent validity checks, demonstrating high content and factorial validity. Convergent and discriminant validity were confirmed using the Average Variance Extracted (AVE) and Fornel-Larcker criterion. Results The findings revealed that professional counsellors exhibited high levels of awareness, acceptability, and application of AI in their counselling practices. Acceptance played a positive mediating role in the relationship between awareness and application. However, male practitioners and professors displayed stronger awareness, acceptance, and application of AI tools compared to their counterparts. Conclusion The study highlights the significant role of acceptance in bridging awareness and application of AI in counselling practices. It underscores the importance of addressing gender and professional rank disparities to ensure equitable adoption and utilization of AI tools. The findings offer valuable insights for policymakers in promoting the integration of AI in counselling to enhance professional practices.
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Affiliation(s)
- Usani Joseph Ofem
- Department of Educational Foundations, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Ebonyi, Nigeria
| | - Pauline Mbua Anake
- Department of Guidance and Counselling, University of Calabar, Calabar, Nigeria
| | - Cyril Bisong Abuo
- Department of Guidance and Counselling, University of Calabar, Calabar, Nigeria
| | - James Omaji Ukatu
- Department of Criminology, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Ebonyi, Nigeria
| | - Eugene Onor Etta
- Department of Public Administration, Federal Polytechnic Ugep, Ugep, Cross River, Nigeria
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Šuto Pavičić J, Marušić A, Buljan I. Using ChatGPT to Improve the Presentation of Plain Language Summaries of Cochrane Systematic Reviews About Oncology Interventions: Cross-Sectional Study. JMIR Cancer 2025; 11:e63347. [PMID: 40106236 PMCID: PMC11939027 DOI: 10.2196/63347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 01/22/2025] [Accepted: 01/27/2025] [Indexed: 03/22/2025] Open
Abstract
Background Plain language summaries (PLSs) of Cochrane systematic reviews are a simple format for presenting medical information to the lay public. This is particularly important in oncology, where patients have a more active role in decision-making. However, current PLS formats often exceed the readability requirements for the general population. There is still a lack of cost-effective and more automated solutions to this problem. Objective This study assessed whether a large language model (eg, ChatGPT) can improve the readability and linguistic characteristics of Cochrane PLSs about oncology interventions, without changing evidence synthesis conclusions. Methods The dataset included 275 scientific abstracts and corresponding PLSs of Cochrane systematic reviews about oncology interventions. ChatGPT-4 was tasked to make each scientific abstract into a PLS using 3 prompts as follows: (1) rewrite this scientific abstract into a PLS to achieve a Simple Measure of Gobbledygook (SMOG) index of 6, (2) rewrite the PLS from prompt 1 so it is more emotional, and (3) rewrite this scientific abstract so it is easier to read and more appropriate for the lay audience. ChatGPT-generated PLSs were analyzed for word count, level of readability (SMOG index), and linguistic characteristics using Linguistic Inquiry and Word Count (LIWC) software and compared with the original PLSs. Two independent assessors reviewed the conclusiveness categories of ChatGPT-generated PLSs and compared them with original abstracts to evaluate consistency. The conclusion of each abstract about the efficacy and safety of the intervention was categorized as conclusive (positive/negative/equal), inconclusive, or unclear. Group comparisons were conducted using the Friedman nonparametric test. Results ChatGPT-generated PLSs using the first prompt (SMOG index 6) were the shortest and easiest to read, with a median SMOG score of 8.2 (95% CI 8-8.4), compared with the original PLSs (median SMOG score 13.1, 95% CI 12.9-13.4). These PLSs had a median word count of 240 (95% CI 232-248) compared with the original PLSs' median word count of 364 (95% CI 339-388). The second prompt (emotional tone) generated PLSs with a median SMOG score of 11.4 (95% CI 11.1-12), again lower than the original PLSs. PLSs produced with the third prompt (write simpler and easier) had a median SMOG score of 8.7 (95% CI 8.4-8.8). ChatGPT-generated PLSs across all prompts demonstrated reduced analytical tone and increased authenticity, clout, and emotional tone compared with the original PLSs. Importantly, the conclusiveness categorization of the original abstracts was unchanged in the ChatGPT-generated PLSs. Conclusions ChatGPT can be a valuable tool in simplifying PLSs as medically related formats for lay audiences. More research is needed, including oversight mechanisms to ensure that the information is accurate, reliable, and culturally relevant for different audiences.
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Affiliation(s)
- Jelena Šuto Pavičić
- Department of Oncology and Radiotherapy, University Hospital of Split, Spinciceva 1, Split, 21000, Croatia, 385 2155817
| | - Ana Marušić
- Department of Research in Biomedicine in Health, Centre for Evidence-based Medicine, University of Split School of Medicine, Split, Croatia
| | - Ivan Buljan
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia
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Mahmoud AK, Farina JM, Pereyra M, Scalia IG, Javadi N, Derakshani D, Elahi AA, Mand K, Suppah M, Abbas MT, Kamal MA, Awad K, Chao CJ, Nkomo VT, Alsidawi S, Lee KS, Lester SJ, Sell-Dottin KA, Fortuin DF, Sweeney JP, Ayoub C, Arsanjani R. To the editor: Artificial intelligence applied to ECG predicts mortality after a transcatheter aortic valve replacement. Prog Cardiovasc Dis 2025:S0033-0620(25)00033-7. [PMID: 40096903 DOI: 10.1016/j.pcad.2025.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Affiliation(s)
| | - Juan M Farina
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | | | | | | | | | - Ali A Elahi
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Katie Mand
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Mustafa Suppah
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | | | - Moaz A Kamal
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Kamal Awad
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Chieh-Ju Chao
- Cardiovascular Department, Mayo Clinic, MN, United States
| | | | - Said Alsidawi
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Kwan S Lee
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | | | | | | | - John P Sweeney
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Chadi Ayoub
- Cardiovascular Department, Mayo Clinic, AZ, United States
| | - Reza Arsanjani
- Cardiovascular Department, Mayo Clinic, AZ, United States.
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Tsuda M, Tsuda K, Asano S, Kato Y, Miyazaki M. Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks (part II). J Neurol Sci 2025; 470:123411. [PMID: 39893881 DOI: 10.1016/j.jns.2025.123411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 01/23/2025] [Accepted: 01/26/2025] [Indexed: 02/04/2025]
Abstract
Neural networks (NNs) possess the capability to learn complex data relationships, recognize inherent patterns by emulating human brain functions, and generate predictions based on novel data. We conducted deep learning utilizing an NN to differentiate between Parkinson's disease (PD) and the parkinsonian variant (MSA-P) of multiple system atrophy (MSA). The distinction between PD and MSA-P in the early stages presents significant challenges. Considering the recently reported heterogeneity and random distribution of lesions in MSA, we performed an analysis employing an NN with voxel-based morphometry data from the entire brain as input variables. The NN's accuracy in distinguishing MSA-P from PD demonstrates sufficient practicality for clinical application.
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Affiliation(s)
- Mitsunori Tsuda
- Neurology Tsuda Clinic, 3006 Hisaishinmachi, Tsu, Mie 514-1118, Japan.
| | - Kenta Tsuda
- Neurology Tsuda Clinic, 3006 Hisaishinmachi, Tsu, Mie 514-1118, Japan
| | - Shingo Asano
- Neurology Tsuda Clinic, 3006 Hisaishinmachi, Tsu, Mie 514-1118, Japan
| | - Yasushi Kato
- Neurology Kato Clinic, 4-5-36 Ichinoki, Ise, Mie 516-0071, Japan
| | - Masao Miyazaki
- Neurology Kato Clinic, 4-5-36 Ichinoki, Ise, Mie 516-0071, Japan
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Barker AP. Artificial intelligence in health education within higher education institutions. Evid Based Nurs 2025:ebnurs-2025-104314. [PMID: 40081867 DOI: 10.1136/ebnurs-2025-104314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2025] [Indexed: 03/16/2025]
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Al-Ghazali MA. Evaluation of Awareness, Perception and Opinions Toward Artificial Intelligence Among Pharmacy Students. Hosp Pharm 2025:00185787251326227. [PMID: 40092293 PMCID: PMC11907559 DOI: 10.1177/00185787251326227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Background: Artificial intelligence (AI) helps to develop personalized medication therapy and regimens. It improves the patient care system. A cross-sectional study used and included pharmacy students, using validated survey questions. Objective: This study aimed to evaluate awareness, perception and opinion toward AI among pharmacy students. Design: This is a cross-sectional study (survey-based). Methods: A cross-sectional survey distribution among students in different levels of the college of pharmacy at National University (NU). The questions were classified to measure the variation of demographics, awareness, perceptions and opinions toward Artificial Intelligence (AI). Results: The results showed that more than 50% of pharmacy students are familiar with the uses of AI and know it's important in scientific research, 46.4% have a basic understanding of AI technologies. However more than 75% don't know the applications of AI used in pharmacy practice, 50.6 % don't know AI can support therapeutic diagnosis and 57 % don't know its importance in pharmacy education. A high perception was shown toward AI in facilitating pharmacy access to information (84.2%) and patients' access to the service (80.8%). In addition, 92% suggested that AI training is needed and 86.1 % recommended using AI in scientific research. The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities.
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Park A, Jung SY, Yune I, Lee HY. Applying Robotic Process Automation to Monitor Business Processes in Hospital Information Systems: Mixed Method Approach. JMIR Med Inform 2025; 13:e59801. [PMID: 40053771 PMCID: PMC11928770 DOI: 10.2196/59801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 01/05/2025] [Accepted: 01/12/2025] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Electronic medical records (EMRs) have undergone significant changes due to advancements in technology, including artificial intelligence, the Internet of Things, and cloud services. The increasing complexity within health care systems necessitates enhanced process reengineering and system monitoring approaches. Robotic process automation (RPA) provides a user-centric approach to monitoring system complexity by mimicking end user interactions, thus presenting potential improvements in system performance and monitoring. OBJECTIVE This study aimed to explore the application of RPA in monitoring the complexities of EMR systems within a hospital environment, focusing on RPA's ability to perform end-to-end performance monitoring that closely reflects real-time user experiences. METHODS The research was conducted at Seoul National University Bundang Hospital using a mixed methods approach. It included the iterative development and integration of RPA bots programmed to simulate and monitor typical user interactions with the hospital's EMR system. Quantitative data from RPA process outputs and qualitative insights from interviews with system engineers and managers were used to evaluate the effectiveness of RPA in system monitoring. RESULTS RPA bots effectively identified and reported system inefficiencies and failures, providing a bridge between end user experiences and engineering assessments. The bots were particularly useful in detecting delays and errors immediately following system updates or interactions with external services. Over 3 years, RPA monitoring highlighted discrepancies between user-reported experiences and traditional engineering metrics, with the bots frequently identifying critical system issues that were not evident from standard component-level monitoring. CONCLUSIONS RPA enhances system monitoring by providing insights that reflect true end user experiences, which are often overlooked by traditional monitoring methods. The study confirms the potential of RPA to act as a comprehensive monitoring tool within complex health care systems, suggesting that RPA can significantly contribute to the maintenance and improvement of EMR systems by providing a more accurate and timely reflection of system performance and user satisfaction.
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Affiliation(s)
- Adam Park
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Se Young Jung
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Family Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Ilha Yune
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Ho-Young Lee
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Nuclear Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea
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Davis VH, Qiang JR, Adekoya MacCarthy I, Howse D, Seshie AZ, Kosowan L, Delahunty-Pike A, Abaga E, Cooney J, Robinson M, Senior D, Zsager A, Aubrey-Bassler K, Irwin M, Jackson LA, Katz A, Marshall EG, Muhajarine N, Neudorf C, Garies S, Pinto AD. Perspectives on Using Artificial Intelligence to Derive Social Determinants of Health Data From Medical Records in Canada: Large Multijurisdictional Qualitative Study. J Med Internet Res 2025; 27:e52244. [PMID: 40053728 PMCID: PMC11926464 DOI: 10.2196/52244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/31/2024] [Accepted: 11/29/2024] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), specifically natural language processing and machine learning, could be used to derive social determinants of health data from electronic medical records. This could reduce the time and resources required to obtain social determinants of health data. OBJECTIVE This study aimed to understand perspectives of a diverse sample of Canadians on the use of AI to derive social determinants of health information from electronic medical record data, including benefits and concerns. METHODS Using a qualitative description approach, in-depth interviews were conducted with 195 participants purposefully recruited from Ontario, Newfoundland and Labrador, Manitoba, and Saskatchewan. Transcripts were analyzed using an inductive and deductive content analysis. RESULTS A total of 4 themes were identified. First, AI was described as the inevitable future, facilitating more efficient, accessible social determinants of health information and use in primary care. Second, participants expressed concerns about potential health care harms and a distrust in AI and public systems. Third, some participants indicated that AI could lead to a loss of the human touch in health care, emphasizing a preference for strong relationships with providers and individualized care. Fourth, participants described the critical importance of consent and the need for strong safeguards to protect patient data and trust. CONCLUSIONS These findings provide important considerations for the use of AI in health care, and particularly when health care administrators and decision makers seek to derive social determinants of health data.
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Affiliation(s)
- Victoria H Davis
- Department of Health Behavior and Health Equity, School of Public Health, University of Michigan-Ann Arbor, Ann Arbor, MI, United States
| | - Jinfan Rose Qiang
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Itunuoluwa Adekoya MacCarthy
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Dana Howse
- Primary Healthcare Research Unit, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada
| | - Abigail Zita Seshie
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Leanne Kosowan
- Department of Family Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Eunice Abaga
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Jane Cooney
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Marjeiry Robinson
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Dorothy Senior
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Alexander Zsager
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
| | - Kris Aubrey-Bassler
- Primary Healthcare Research Unit, Memorial University of Newfoundland and Labrador, St. John's, NL, Canada
| | - Mandi Irwin
- Department of Family Medicine, Dalhousie University, Halifax, NS, Canada
| | - Lois A Jackson
- School of Health and Human Performance, Dalhousie University, Halifax, NS, Canada
| | - Alan Katz
- Department of Family Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Nazeem Muhajarine
- Department of Community Health & Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Cory Neudorf
- Department of Community Health & Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Stephanie Garies
- Department of Family Medicine, University of Calgary, Calgary, Canada
| | - Andrew D Pinto
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, St. Michael's Hospital, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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40
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Adhikari S, Ahmed I, Bajracharya D, Khanal B, Solomon C, Jayaratne K, Mamum KAA, Talukder MSH, Shakya S, Manandhar S, Memon ZA, Chowdhury MH, Ul Islam I, Rakhshani NS, Khan MI. Transforming healthcare through just, equitable and quality driven artificial intelligence solutions in South Asia. NPJ Digit Med 2025; 8:139. [PMID: 40038520 PMCID: PMC11880425 DOI: 10.1038/s41746-025-01534-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Accepted: 02/20/2025] [Indexed: 03/06/2025] Open
Abstract
AI can transform healthcare in LMICs by improving access, reducing costs, and enhancing efficiency. However, challenges such as safety, bias, and the resource constraints need to be addressed. Further, collaboration across domains is essential to develop capacity, user-friendly tools, and training. Ethical considerations should be central to AI deployment. By emphasizing gender equity, fairness, and responsible design, LMICs can harness AI's power to enhance healthcare outcomes and advance equitable care.
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Grants
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
- 110066 - 001. International Development Research Centre, Ottawa, Canada, for AI4GH:
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Affiliation(s)
| | - Iftikhar Ahmed
- University of Europe for Applied Sciences, Potsdam, Germany
| | | | - Bishesh Khanal
- Nepal Applied Mathematics and Informatics Institute for Research (NAAMII), Lalitpur, Nepal
| | | | | | | | | | | | | | | | | | | | | | - M Imran Khan
- Precision Health Consultants (PHC) Global (Private) Limited, Karachi, Pakistan.
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Zhu Z, Wang Y, Qi Z, Hu W, Zhang X, Wagner SK, Wang Y, Ran AR, Ong J, Waisberg E, Masalkhi M, Suh A, Tham YC, Cheung CY, Yang X, Yu H, Ge Z, Wang W, Sheng B, Liu Y, Lee AG, Denniston AK, Wijngaarden PV, Keane PA, Cheng CY, He M, Wong TY. Oculomics: Current concepts and evidence. Prog Retin Eye Res 2025; 106:101350. [PMID: 40049544 DOI: 10.1016/j.preteyeres.2025.101350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2024] [Revised: 03/03/2025] [Accepted: 03/03/2025] [Indexed: 03/20/2025]
Abstract
The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.
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Affiliation(s)
- Zhuoting Zhu
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia.
| | - Yueye Wang
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Ziyi Qi
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia; Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai, China
| | - Wenyi Hu
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Siegfried K Wagner
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London, UK
| | - Yujie Wang
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, USA
| | - Ethan Waisberg
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Mouayad Masalkhi
- University College Dublin School of Medicine, Belfield, Dublin, Ireland
| | - Alex Suh
- Tulane University School of Medicine, New Orleans, LA, USA
| | - Yih Chung Tham
- Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Xiaohong Yang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yun Liu
- Google Research, Mountain View, CA, USA
| | - Andrew G Lee
- Center for Space Medicine and the Department of Ophthalmology, Baylor College of Medicine, Houston, USA; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, USA; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, USA; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, USA; Department of Ophthalmology, University of Texas Medical Branch, Galveston, USA; University of Texas MD Anderson Cancer Center, Houston, USA; Texas A&M College of Medicine, Bryan, USA; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, USA
| | - Alastair K Denniston
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London, UK; National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre (BRC), University Hospital Birmingham and University of Birmingham, Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Peter van Wijngaarden
- Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London, UK
| | - Ching-Yu Cheng
- Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China; Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China; Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China.
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Jo SJ, Rhu J, Kim J, Choi GS, Joh JW. Indication model for laparoscopic repeat liver resection in the era of artificial intelligence: machine learning prediction of surgical indication. HPB (Oxford) 2025:S1365-182X(25)00075-9. [PMID: 40090778 DOI: 10.1016/j.hpb.2025.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 01/11/2025] [Accepted: 02/28/2025] [Indexed: 03/18/2025]
Abstract
BACKGROUND Laparoscopic repeat liver resection (LRLR) is still a challenging technique and requires a careful selection of indications. However, the current difficulty scoring system is not suitable for selecting indications. The purpose of this study is to develop the indication model for LRLR using machine learning and to identify factors associated with open conversion (OC). METHODS Patients who underwent repeat hepatectomy (2017-2021) at Samsung Medical Center 2021 were investigated. Multiple indication models were developed using machine learning techniques (random forest, SVM, XGB) and logistic regression. The predictive performance of these models was compared, and risk factors associated with OC were analyzed. RESULTS Among 221 patients (110 LRLR, 111 ORLR), the ORLR group had a higher previous open approach rate (75.7% vs. 38.2%, p<0.001). Twice previous abdominal surgery was the only independent OC risk factor (OR 6.56, p=0.009). The indication model showed moderate predictive power (random forest AUC=0.779, logistic regression AUC=0.725, p=0.710). Important variables were previous laparoscopic approach, present subsegmentectomy, and left-sided tumor location. CONCLUSION The performance of the indication model for LRLR showed moderate predictive power in both machine learning and logistic regression. The important variables for LRLR were previous laparoscopic approach, present subsegmentectomy, and left side location.
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Affiliation(s)
- Sung Jun Jo
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jinsoo Rhu
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| | - Jongman Kim
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Gyu-Seong Choi
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
| | - Jae-Won Joh
- Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Goh E, Bunning B, Khoong EC, Gallo RJ, Milstein A, Centola D, Chen JH. Physician clinical decision modification and bias assessment in a randomized controlled trial of AI assistance. COMMUNICATIONS MEDICINE 2025; 5:59. [PMID: 40038550 PMCID: PMC11880198 DOI: 10.1038/s43856-025-00781-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 02/21/2025] [Indexed: 03/06/2025] Open
Abstract
BACKGROUND Artificial intelligence assistance in clinical decision making shows promise, but concerns exist about potential exacerbation of demographic biases in healthcare. This study aims to evaluate how physician clinical decisions and biases are influenced by AI assistance in a chest pain triage scenario. METHODS A randomized, pre post-intervention study was conducted with 50 US-licensed physicians who reviewed standardized chest pain video vignettes featuring either a white male or Black female patient. Participants answered clinical questions about triage, risk assessment, and treatment before and after receiving GPT-4 generated recommendations. Clinical decision accuracy was evaluated against evidence-based guidelines. RESULTS Here we show that physicians are willing to modify their clinical decisions based on GPT-4 assistance, leading to improved accuracy scores from 47% to 65% in the white male patient group and 63% to 80% in the Black female patient group. The accuracy improvement occurs without introducing or exacerbating demographic biases, with both groups showing similar magnitudes of improvement (18%). A post-study survey indicates that 90% of physicians expect AI tools to play a significant role in future clinical decision making. CONCLUSIONS Physician clinical decision making can be augmented by AI assistance while maintaining equitable care across patient demographics. These findings suggest a path forward for AI clinical decision support that improves medical care without amplifying healthcare disparities.
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Affiliation(s)
- Ethan Goh
- Stanford Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
- Stanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA.
| | - Bryan Bunning
- Stanford Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Elaine C Khoong
- Division of General Internal Medicine (DGIM) at ZSFG, Department of Medicine, UCSF, San Francisco, CA, USA
- Division of Clinical Informatics and Digital Transformation, UCSF, San Francisco, CA, USA
- UCSF Action Research Center for Equity, UCSF, San Francisco, CA, USA
| | - Robert J Gallo
- Stanford Biomedical Informatics Research, Stanford University, Stanford, CA, USA
- Center for Innovation to Implementation, VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Arnold Milstein
- Stanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
| | - Damon Centola
- Communication, Sociology and Engineering, University of Pennsylvania, Pennsylvania, PA, USA
| | - Jonathan H Chen
- Stanford Biomedical Informatics Research, Stanford University, Stanford, CA, USA
- Stanford Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
- Division of Hospital Medicine, Stanford University, Stanford, CA, USA
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Herbozo Contreras LF, Cui J, Yu L, Huang Z, Nikpour A, Kavehei O. KAN-EEG: towards replacing backbone-MLP for an effective seizure detection system. ROYAL SOCIETY OPEN SCIENCE 2025; 12:240999. [PMID: 40078924 PMCID: PMC11898101 DOI: 10.1098/rsos.240999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Revised: 10/25/2024] [Accepted: 01/24/2025] [Indexed: 03/14/2025]
Abstract
The landscape of artificial intelligence (AI) research is witnessing a transformative shift with the emergence of the Kolmogorov-Arnold network (KAN), presenting a novel architectural paradigm aimed to redefine the structural foundations of AI models, which are based on multilayer perceptron (MLP). Through rigorous experimentation and evaluation, we introduce the KAN-electroencephalogram (EEG) model, a tailored design for efficient seizure detection. Our proposed network is tested and successfully generalized on three different datasets, one from the USA, one from Europe, and one from Oceania, recorded with different front-end hardware. All datasets are scalp EEG in adults and are from patients living with epilepsy. Our empirical findings reveal that while both architectures demonstrate commendable performance in seizure detection, the KAN model exhibits high-level out-of-sample generalization across datasets from diverse geographical regions, underscoring its inherent efficacy and adaptability at the backbone level. Furthermore, we demonstrate the resilience of the KAN architecture to model size reduction and shallow network configurations, highlighting its versatility and efficiency by preventing over-fitting in-sample datasets. This study advances our understanding of innovative neural network architectures and underscores the pioneering potential of KANs in critical domains such as medical diagnostics.
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Affiliation(s)
| | - Jiashuo Cui
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW2006, Australia
| | - Leping Yu
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW2006, Australia
| | - Zhaojing Huang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW2006, Australia
| | - Armin Nikpour
- Comprehensive Epilepsy Service and Department of Neurology, Royal Prince Alfred Hospital, Sydney, NSW2050, Australia
- Faculty of Medicine and Health, Central Clinical School, The University of Sydney, Sydney, NSW2006, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, NSW2006, Australia
- The University of Sydney Nano Institute, Sydney, NSW2006, Australia
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Pantanowitz L, Pearce T, Abukhiran I, Hanna M, Wheeler S, Soong TR, Tafti AP, Pantanowitz J, Lu MY, Mahmood F, Gu Q, Rashidi HH. Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning. Mod Pathol 2025; 38:100680. [PMID: 39675426 DOI: 10.1016/j.modpat.2024.100680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Revised: 11/26/2024] [Accepted: 11/27/2024] [Indexed: 12/17/2024]
Abstract
The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such nongenerative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (ie, classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based nongenerative foundation models. Unsupervised learning techniques, including clustering, dimensionality reduction, and anomaly detection, are also discussed for their roles in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of nongenerative AI algorithms for analyzing whole slide images are also highlighted. The performance, explainability, and reliability of nongenerative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.
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Affiliation(s)
- Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania.
| | - Thomas Pearce
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Ibrahim Abukhiran
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Matthew Hanna
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Sarah Wheeler
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - T Rinda Soong
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Ahmad P Tafti
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania; Health Informatics, School of Health and Rehabilitation Services, University of Pittsburgh, Pittsburgh, Pennsylvania
| | | | - Ming Y Lu
- Department of Pathology, Massachusetts General Brigham Hospital, Harvard Medical School, Boston, Massachusetts; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Faisal Mahmood
- Department of Pathology, Massachusetts General Brigham Hospital, Harvard Medical School, Boston, Massachusetts; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Qiangqiang Gu
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania
| | - Hooman H Rashidi
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania.
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Rujas M, Martín Gómez Del Moral Herranz R, Fico G, Merino-Barbancho B. Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications. Int J Med Inform 2025; 195:105763. [PMID: 39719743 DOI: 10.1016/j.ijmedinf.2024.105763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 12/11/2024] [Accepted: 12/14/2024] [Indexed: 12/26/2024]
Abstract
BACKGROUND The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data collection and regulatory constraints, for which synthetic data is an emerging alternative. While previous research has reviewed synthetic data generation techniques, there is limited focus on their applications and the motivations driving their synthesis. A comprehensive review is needed to expand the potential of synthetic data into less explored healthcare areas. OBJECTIVE This review aims to identify the healthcare domains where synthetic data are currently generated, the motivations behind their creation, their future uses, limitations, and types of data. MATERIALS AND METHODS Following the PRISMA-ScR framework, this review analysed literature from the last 10 years within PubMed, Scopus, and Web of Science. Reviews containing information on synthetic data generation in healthcare were screened and analysed. Key healthcare domains, motivations, future uses, and gaps in the literature were identified through a structured data extraction process. RESULTS Of the 346 reviews identified, 42 were included for data extraction. Thirteen main domains were identified, with Oncology, Neurology, and Cardiology being the most frequently mentioned. Five primary motivations for synthetic data generation and three major categories of future applications were highlighted. Additionally, unstructured data, particularly images, were found to be the predominant type of synthetic data generated. DISCUSSION AND CONCLUSION Synthetic data are currently being generated across diverse healthcare domains, showcasing their adaptability and potential. Despite their early stage, synthetic data technologies hold significant promise for future applications. Expanding their use into new domains and less common data types (e.g., video and text) could further enhance their impact. Future work should focus on developing evaluation benchmarks and standardized generative models tailored to specific healthcare domains.
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Affiliation(s)
- Miguel Rujas
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040 Madrid, Spain.
| | | | - Giuseppe Fico
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040 Madrid, Spain
| | - Beatriz Merino-Barbancho
- Life Supporting Technologies Research Group, Universidad Politécnica de Madrid, Avda Complutense 30, 28040 Madrid, Spain
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Jessica H, Britney R, Sarira ED, Parisa A, Joe Z, Betty B C. Applications of artificial intelligence in current pharmacy practice: A scoping review. Res Social Adm Pharm 2025; 21:134-141. [PMID: 39730225 DOI: 10.1016/j.sapharm.2024.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 10/13/2024] [Accepted: 12/16/2024] [Indexed: 12/29/2024]
Abstract
BACKGROUND Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice. METHODS A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: ("artificial intelligence") AND ("pharmacy" OR "pharmacist" OR "pharmaceutical service" OR "pharmacy service"). Reference lists of identified review articles were also screened. RESULTS The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes. CONCLUSIONS This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.
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Affiliation(s)
- Hatzimanolis Jessica
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - Riley Britney
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - El-Den Sarira
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
| | - Aslani Parisa
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia.
| | | | - Chaar Betty B
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia
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Goudman L, Moens M. The framework of chronic pain management: Redefining the outcomes. J Clin Anesth 2025; 103:111799. [PMID: 40024090 DOI: 10.1016/j.jclinane.2025.111799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2024] [Revised: 02/04/2025] [Accepted: 02/25/2025] [Indexed: 03/04/2025]
Affiliation(s)
- Lisa Goudman
- STIMULUS Research Group, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; Cluster Neurosciences, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Pain in Motion (PAIN) Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Research Foundation-Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium; Florida Atlantic University, 777 Glades Road, BC-71, Boca Raton, FL 33431, USA.
| | - Maarten Moens
- STIMULUS Research Group, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Neurosurgery, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium; Cluster Neurosciences, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Pain in Motion (PAIN) Research Group, Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Research Foundation-Flanders (FWO), Leuvenseweg 38, 1000 Brussels, Belgium; Department of Radiology, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium
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Dibbets AC, Koldeweij C, Osinga EP, Scheepers HCJ, de Wildt SN. Barriers and Facilitators for Bringing Model-Informed Precision Dosing to the Patient's Bedside: A Systematic Review. Clin Pharmacol Ther 2025; 117:633-645. [PMID: 39659053 PMCID: PMC11835426 DOI: 10.1002/cpt.3510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 11/11/2024] [Indexed: 12/12/2024]
Abstract
Model-informed precision dosing (MIPD) utilizes mathematical models to predict optimal medication doses for a specific patient or patient population. However, the factors influencing the implementation of MIPD have not been fully elucidated, hindering its widespread use in clinical practice. A systematic review was conducted in PubMed from inception to December 2022, aiming to identify barriers and facilitators for the implementation of MIPD into patient care. Articles with a focus on implementation of MIPD were eligible for this review. After screening titles and abstracts, full articles investigating the clinical implementation of MIPD were included for data extraction. Of 790 records identified, 15 publications were included. A total of 72 barriers and facilitators across seven categories were extracted through a hybrid thematic analysis. Barriers comprised limited data for model validation, unclear regulatory pathways for model endorsement and additional drug level measurements required for certain types of MIPD. Facilitators encompassed the development of user-friendly MIPD tools continuously updated based on user feedback and data. Collaborative efforts among diverse stakeholders for model validation and implementation, along with education of end-users, may promote the utilization of MIPD in patient care. Despite ongoing challenges, this systematic review revealed various strategies to facilitate the clinical implementation of MIPD.
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Affiliation(s)
- Anna Caroline Dibbets
- Division of Pharmacology and Toxicology, Department of PharmacyRadboud University Medical CenterNijmegenThe Netherlands
- Department of Obstetrics and GynaecologyMaastricht University Medical CenterMaastrichtThe Netherlands
- GROW, Institute for Oncology and ReproductionMaastrichtThe Netherlands
| | - Charlotte Koldeweij
- Division of Pharmacology and Toxicology, Department of PharmacyRadboud University Medical CenterNijmegenThe Netherlands
| | - Esra P. Osinga
- Division of Pharmacology and Toxicology, Department of PharmacyRadboud University Medical CenterNijmegenThe Netherlands
| | - Hubertina C. J. Scheepers
- Department of Obstetrics and GynaecologyMaastricht University Medical CenterMaastrichtThe Netherlands
- GROW, Institute for Oncology and ReproductionMaastrichtThe Netherlands
| | - Saskia N. de Wildt
- Division of Pharmacology and Toxicology, Department of PharmacyRadboud University Medical CenterNijmegenThe Netherlands
- Department of Pediatric and Neonatal Intensive CareErasmus MC‐Sophia Children's HospitalRotterdamThe Netherlands
- Department of Intensive CareRadboud University Medical CenterNijmegenThe Netherlands
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Lim B, Lirios G, Sakalkale A, Satheakeerthy S, Hayes D, Yeung JMC. Assessing the efficacy of artificial intelligence to provide peri-operative information for patients with a stoma. ANZ J Surg 2025; 95:464-496. [PMID: 39620607 DOI: 10.1111/ans.19337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 10/11/2024] [Accepted: 11/17/2024] [Indexed: 03/27/2025]
Abstract
BACKGROUND Stomas present significant lifestyle and psychological challenges for patients, requiring comprehensive education and support. Current educational methods have limitations in offering relevant information to the patient, highlighting a potential role for artificial intelligence (AI). This study examined the utility of AI in enhancing stoma therapy management following colorectal surgery. MATERIAL AND METHODS We compared the efficacy of four prominent large language models (LLM)-OpenAI's ChatGPT-3.5 and ChatGPT-4.0, Google's Gemini, and Bing's CoPilot-against a series of metrics to evaluate their suitability as supplementary clinical tools. Through qualitative and quantitative analyses, including readability scores (Flesch-Kincaid, Flesch-Reading Ease, and Coleman-Liau index) and reliability assessments (Likert scale, DISCERN score and QAMAI tool), the study aimed to assess the appropriateness of LLM-generated advice for patients managing stomas. RESULTS There are varying degrees of readability and reliability across the evaluated models, with CoPilot and ChatGPT-4 demonstrating superior performance in several key metrics such as readability and comprehensiveness. However, the study underscores the infant stage of LLM technology in clinical applications. All responses required high school to college level education to comprehend comfortably. While the LLMs addressed users' questions directly, the absence of incorporating patient-specific factors such as past medical history generated broad and generic responses rather than offering tailored advice. CONCLUSION The complexity of individual patient conditions can challenge AI systems. The use of LLMs in clinical settings holds promise for improving patient education and stoma management support, but requires careful consideration of the models' capabilities and the context of their use.
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Affiliation(s)
- Bryan Lim
- Department of Colorectal Surgery, Western Health, Melbourne, Australia
| | - Gabriel Lirios
- Department of Colorectal Surgery, Western Health, Melbourne, Australia
| | - Aditya Sakalkale
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
| | | | - Diana Hayes
- Department of Colorectal Surgery, Western Health, Melbourne, Australia
| | - Justin M C Yeung
- Department of Colorectal Surgery, Western Health, Melbourne, Australia
- Department of Surgery, Western Precinct, University of Melbourne, Melbourne, Australia
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