The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.

We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.

A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.

Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).

The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

Artificial intelligence (AI) has recently advanced in medicine globally, transforming healthcare delivery and medical education. While AI integration into medical curricula is gaining momentum worldwide, research on medical students' preparedness remains limited, particularly in developing countries. This paper aims to investigate the readiness of medical students at the Kerman University of Medical Sciences to employ AI in medicine in 2022.

This cross-sectional research was carried out by distributing the validated 20-item Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) among 360 medical students, with a response rate of 94% (n = 340). The MAIRS-MS assessed four domains, including cognition (8 items), ability (7 items), vision (2 items), and ethics (3 items), using a 5-point Likert scale. Data analysis was conducted by descriptive statistics and independent sample t-tests in SPSS v24.0, considering p < 0.05 significant.

Participants demonstrated below-average readiness scores across all domains: ability (M = 21.88 ± 6.74, 62.5% of the maximum possible score), cognition (M = 20.30 ± 7.04, 50.8%), ethics (M = 10.94 ± 3.04, 72.9%), and vision (M = 6.09 ± 1.94, 60.9%). The total mean readiness score was 59.21 ± 16.12 (59.2% of the maximum). The highest and lowest-rated items were "value of AI in education" (3.96 ± 1.18) and "explaining AI system training" (2.10 ± 1.01), respectively. No significant differences were found across demographic factors (p > 0.05).

Iranian medical students currently show limited readiness for AI integration in healthcare practice. Therefore, the study recommends: (1) implementing structured introductory AI courses in medical curricula, focusing particularly on technical fundamentals and practical applications, and (2) developing hands-on training programs that combine AI concepts with clinical scenarios. These findings provide valuable insights for curriculum development and educational policy in medical education.

Managing obesity requires a comprehensive approach that involves therapeutic lifestyle changes, medications, or metabolic surgery. Many patients seek health information from online sources and artificial intelligence models like ChatGPT, Google Gemini, and Microsoft Copilot before consulting health professionals. This study aims to evaluate the appropriateness of the responses of Google Gemini and Microsoft Copilot to questions on pharmacologic and surgical management of obesity and assess for bias in their responses to either the ADA or AACE guidelines.

Ten questions were compiled into a set and posed separately to the free editions of Google Gemini and Microsoft Copilot. Recommendations for the questions were extracted from the ADA and the AACE websites, and the responses were graded by reviewers for appropriateness, completeness, and bias to any of the guidelines.

All responses from Microsoft Copilot and 8/10 (80%) responses from Google Gemini were appropriate. There were no inappropriate responses. Google Gemini refused to respond to two questions and insisted on consulting a physician. Microsoft Copilot (10/10; 100%) provided a higher proportion of complete responses than Google Gemini (5/10; 50%). Of the eight responses from Google Gemini, none were biased towards any of the guidelines, while two of the responses from Microsoft Copilot were biased.

The study highlights the role of Microsoft Copilot and Google Gemini in weight loss management. The differences in their responses may be attributed to the variation in the quality and scope of their training data and design.

Metabolic syndrome (MetS) is a progressive chronic pathophysiological state characterised by abdominal obesity, hypertension, hyperglycaemia, and dyslipidaemia. It is recognised as one of the major clinical syndromes affecting human health, with approximately one-quarter of the global population impacted. MetS increases the risk of developing cardiovascular diseases (CVDs), stroke, type 2 diabetes mellitus (T2DM), and diverse metabolic diseases. Early diagnosis of MetS could potentially reduce the prevalence of these diseases. However, care for the MetS population faces significant challenges due to (i) a lack of comprehensive understanding of the full spectrum of associated diseases, stemming from unclear pathophysiological mechanisms and (ii) frequent underdiagnosis or misdiagnosis of MetS in clinical settings due to inconsistent screening guidelines, limited medical resources, time constraints in clinical practice, and insufficient awareness and training. The increasing availability of healthcare and medical data presents opportunities to apply and innovate with artificial intelligence (AI) in addressing these challenges. This review aims to (i) summarise the spectrum of diseases associated with MetS and (ii) review the diverse AI models applied to MetS and metabolic syndrome-related diseases (MetSRD), where MetSRD collectively refers to diseases and conditions directly associated with MetS.

Our review consists of two phases. Initially, we conducted a literature review on MetS to narrow down the spectrum of MetSRD based on the strength of clinical evidence. We then used the terms 'Metabolic Syndrome' and 'Machine Learning' in combination with the identified MetSRD for further refinement. In total, we identified 52 related diseases in the first phase and 36 articles in the second phase.

We identified a total of 52 MetSRD after the first phase, with T2DM, CVDs, and cancer being the top three. Among the 36 articles obtained in the second phase, we observed the following: (i) The criteria for MetS were inconsistent across the studies. (ii) The primary purpose of AI applications was to identify risk factors for diseases, thereby improving predictions for MetS or MetSRD. Traditional machine learning models, such as Random Forest and Logistic Regression, were found to be the most effective. (iii) In addition to the MetS criteria, AI models explored other factors, including demographic and physiological variables, dietary influences, lipidomic and proteomic indicators, and more.

This review underscores the significant link between MetS and a spectrum of diseases, with a particular focus on underreported conditions such as non-alcoholic fatty liver disease and stroke. Through the analysis of data from diverse sources, AI models, and MetS diagnostic criteria, additional indicators beyond traditional measures have been identified, emphasising the importance of combining both traditional and non-traditional markers to enhance the diagnostic and predictive capabilities for MetS and MetSRD. AI shows great potential in MetS research, particularly through the integration of multi-source data, including clinical metrics, genetic information, and omics data. The amalgamation of traditional machine learning and modern machine learning models is particularly promising, offering a balanced approach to model performance and data complexity. While international definitions provide global applicability, they may not be suitable for all populations and scenarios, necessitating flexible diagnostic criteria and adaptive, explainable algorithms. Ultimately, these will enable personalised diagnostics and targeted interventions.

This study explores a comprehensive 4-year retrospective analysis of 511 forensic consultations conducted at "Policlinico" hospital in Bari, Italy. It highlights the expanding role of legal medicine within healthcare settings, an area that has traditionally been limited to expert testimony and forensic pathology. Over time, legal medicine in Italy has evolved to address a variety of clinical areas, including informed consent, disability assessment, personal injuries, and sexual violence. This research aims to examine these diverse applications and their impact on patient care.

Data were systematically categorized and analyzed using a multivariate multinomial regression model. The study focused on key variables, such as patient demographics and timing of shifts, to identify significant determinants that influence the types of forensic consultations conducted. The dataset consisted of 511 consultations, covering a range of clinical and legal issues.

The analysis revealed that informed consent issues were the most prominent, with 58.7% of consultations addressing concerns related to patient autonomy and the capacity for consent, particularly in neuropsychiatric conditions. Personal injury consultations accounted for 24.3% of the total, and sexual assault cases made up 10%. These results underscore the intersection of medical practice and forensic evaluation, highlighting societal issues such as interpersonal violence and the importance of legal medicine in healthcare.

The findings highlight significant gaps in the literature regarding the broader applications of legal medicine, especially in terms of integrating advanced methodologies like artificial intelligence. Such technologies could enhance patient profiling and predictive care, ultimately improving patient safety, risk management, and the protection of patient rights. The study advocates for structured forensic consultation services to be incorporated into clinical practice, emphasizing the role of legal medicine in improving patient-centered care and promoting justice. These insights are crucial for healthcare professionals, administrators, and policymakers aiming to optimize healthcare systems.

Background: Artificial intelligence (AI) is increasingly shaping the landscape of emergency surgery by offering real-time decision support, enhancing diagnostic accuracy, and optimizing workflows. However, its implementation raises significant ethical concerns, particularly regarding accountability, transparency, patient autonomy, and bias. Objective: This perspective paper, grounded in a narrative review, explores the ethical dilemmas associated with AI in emergency surgery and proposes future directions for its responsible and equitable integration. Methods: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and Google Scholar, covering the literature published from January 2010 to December 2024. We focused on peer-reviewed articles discussing AI in surgical or emergency care and highlighting ethical, legal, or regulatory issues. A thematic analysis was used to synthesize the main ethical challenges. Results: Key ethical concerns identified include issues of accountability in AI-assisted decision-making, the "black box" effect and bias in algorithmic design, data privacy and protection, and the lack of global regulatory coherence. Thematic domains were developed around autonomy, beneficence, justice, transparency, and informed consent. Conclusions: Responsible AI implementation in emergency surgery requires transparent and explainable models, diverse and representative datasets, robust consent frameworks, and clear guidelines for liability and oversight. Interdisciplinary collaboration is essential to align technological innovation with patient-centered and ethically sound clinical practice.

The field of artificial intelligence (AI) is expanding quickly. To mimic the structure and biological evolution of the human brain, AI was developed to enable computers to acquire knowledge and manipulate their surroundings. There have been notable developments in the use of AI in healthcare; it can enhance diagnosis and treatment in various medical specialties. The cost of prompt diagnosis and treatment is hampered by the absence of efficient, dependable, and reasonably priced detection and real-time monitoring. Smart health tracking systems integrating AI and nanoscience are an emerging frontier that solves these obstacles. Targeted delivery of drug systems, biosensing, imaging, and other diagnostic and therapeutic fields can widely benefit abundantly from nanoscience in healthcare. AI technology has the potential to expand biomedical applications by analyzing and interpreting biological data, speeding up drug discovery, and identifying novel molecules with predictive behavior. This review outlines the current obstacles and potential opportunities for delivering personal healthcare using AI-assisted clinical decision support systems.

Microbiome-based dietary supplements have gained attention for their role in enhancing brain development and cognitive health. The gut microbiome influences neurological functions through the gut-brain axis, impacting neurotransmitter production, immune regulation, and metabolic pathways. Dysbiosis is linked to neurological disorders such as Alzheimer's, Parkinson's, and autism spectrum disorders. This chapter explores dietary interventions targeting the microbiome, emphasising probiotics, prebiotics, and postbiotics. Additionally, AI and machine learning are transforming microbiome research by enabling personalised supplementation strategies tailored to individual gut profiles. Ethical challenges, including data privacy and algorithmic bias, are also discussed. Advances in big data analytics and predictive modelling are paving the way for precision-targeted interventions to optimise brain health. While microbiome-based therapies hold great promise, further clinical validation and regulatory frameworks are needed to ensure their efficacy and accessibility. This chapter highlights the future potential of microbiome-targeted strategies in neuroprotection and cognitive well-being.

The prevalence of mental health difficulties among young people has risen in recent years, with 75% of mental disorders emerging before the age of 24 years. The identification and treatment of mental health issues earlier in life improves later-life outcomes. The COVID-19 pandemic spurred the growth of digital mental health interventions (DMHIs), which offer accessible support. However, young people of different ethnicities face barriers to DMHIs, such as socioeconomic disadvantage and cultural stigma.

This review aimed to summarize and evaluate the engagement with and effectiveness of DMHIs among young people of different ethnicities.

A systematic search was conducted in MEDLINE, Embase, and PsycINFO for studies published between January 2019 and May 2024, with an update in September 2024. The inclusion criteria were participants aged <25 years using DMHIs from various ethnic backgrounds. Three reviewers independently screened and selected the studies. Data on engagement (eg, use and uptake) and effectiveness (eg, clinical outcomes and symptom improvement) were extracted and synthesized to compare findings. Studies were assessed for quality using the Mixed Methods Appraisal Tool.

The final search yielded 67 studies, of which 7 (10%) met inclusion criteria. There were 1853 participants across the 7 studies, all from high-income countries. Participants were predominantly aged 12 to 25 years, with representation of diverse ethnic identities, including Black, Asian, Hispanic, mixed race, and Aboriginal individuals. Engagement outcomes varied, with culturally relatable, low-cost interventions showing higher retention and user satisfaction. Linguistic barriers and country of origin impeded the effectiveness of some interventions, while near-peer mentorship, coproduction, and tailored content improved the effectiveness of DMHIs. While initial results are promising, small sample sizes, heterogeneity in outcome assessments, and a paucity of longitudinal data impeded robust comparisons and generalizability.

DMHIs show potential as engaging and effective mental health promotional tools for young people of different ethnicities, especially when coproduced and culturally relatable. Initial data suggest that interventions facilitating near-peer mentoring, linguistic adaptation, low cost, and cultural relatability have improved engagement and effectiveness. Future research should focus on developing a consensus definition of DMHIs, exploring DMHIs in children aged <12 years, and conducting detailed qualitative and quantitative research on use factors and treatment efficacy of DMHIs for young people of different ethnicities.

PROSPERO CRD42024544364; https://tinyurl.com/yk5jt8yk.

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.

Evaluate RAPIDx AI's integration into clinical workflows, address usability barriers, and optimise its adoption in emergencies.

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.

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.

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.

Cardiovascular diseases (CVDs) remain the leading global cause of mortality, and a high prevalence of cardiac conditions, including premature deaths, have increased from decades until today. However, early detection and management of these conditions are challenging, given their complexity, the scale of affected populations, the dynamic nature of the disease process, and the treatment approach. The transformative potential is being brought by Artificial Intelligence (AI), specifically machine learning (ML) and deep learning technologies, to analyze massive datasets, improve diagnostic accuracy, and optimize treatment strategy. The recent advancements in such AI-based frameworks as the personalization of decision-making support systems for customized medicine automated image assessments drastically increase the precision and efficiency of healthcare professionals. However, implementing AI is widely clogged with obstacles, including regulatory, privacy, and validation across populations. Additionally, despite the desire to incorporate AI into clinical routines, there is no shortage of concern about interoperability and clinician acceptance of the system. Despite these challenges, further research and development are essential for overcoming these hurdles. This review explores the use of AI in cardiovascular care, its limitations for current use, and future integration toward better patient outcomes.

The rapid advancement of artificial intelligence (AI), particularly in large language models like ChatGPT and Google's Gemini AI, marks a transformative era in technological innovation. This study explores the potential of AI in ophthalmology, focusing on the capabilities of ChatGPT and Gemini AI. While these models hold promise for medical education and clinical support, their integration requires comprehensive evaluation. This research aims to bridge a gap in the literature by comparing Gemini AI and ChatGPT, assessing their performance against ophthalmology residents using a dataset derived from ophthalmology board exams.

A dataset comprising 600 questions across 12 subspecialties was curated from Israeli ophthalmology residency exams, encompassing text and image-based formats. Four AI models - ChatGPT-3.5, ChatGPT-4, Gemini, and Gemini Advanced - underwent testing on this dataset. The study includes a comparative analysis with Israeli ophthalmology residents, employing specific metrics for performance assessment.

Gemini Advanced demonstrated superior performance with a 66% accuracy rate. Notably, ChatGPT-4 exhibited improvement at 62%, Gemini at 58%, and ChatGPT-3.5 served as the reference at 46%. Comparative analysis with residents offered insights into AI models' performance relative to human-level medical knowledge. Further analysis delved into yearly performance trends, topic-specific variations, and the impact of images on chatbot accuracy.

The study unveils nuanced AI model capabilities in ophthalmology, emphasizing domain-specific variations. The superior performance of Gemini Advanced superior performance indicates significant advancements, while ChatGPT-4's improvement is noteworthy. Both Gemini and ChatGPT-3.5 demonstrated commendable performance. The comparative analysis underscores AI's evolving role as a supplementary tool in medical education. This research contributes vital insights into AI effectiveness in ophthalmology, highlighting areas for refinement. As AI models evolve, targeted improvements can enhance adaptability across subspecialties, making them valuable tools for medical professionals and enriching patient care.

What is known AI breakthroughs, like ChatGPT and Google's Gemini AI, are reshaping healthcare. In ophthalmology, AI integration has overhauled clinical workflows, particularly in analyzing images for diseases like diabetic retinopathy and glaucoma. What is new This study presents a pioneering comparison between Gemini AI and ChatGPT, evaluating their performance against ophthalmology residents using a meticulously curated dataset derived from real-world ophthalmology board exams. Notably, Gemini Advanced demonstrates superior performance, showcasing substantial advancements, while the evolution of ChatGPT-4 also merits attention. Both models exhibit commendable capabilities. These findings offer crucial insights into the efficacy of AI in ophthalmology, shedding light on areas ripe for further enhancement and optimization.

In recent years, a sharp increase in artificial intelligence (AI)-based software as medical devices has been seen in the United States and the European Union. Despite the huge potential of these devices in alleviating suffering through rapid identification and early intervention, their adoption in clinical practice has remained relatively slow due to ethical questions surrounding their usage. Even though there is no universal framework for the approval of these devices, the guiding principles behind individual regulatory bodies almost stay the same, with some more focused on the technical aspect while others involving the ethical aspects as well. The International Medical Device Regulators Forum devised a SaMD Working Group to outline the essential controls guiding the approval of these devices, but there is a lack of a structured approach for the regulatory approval process. This article outlines the principles of medical ethics, such as autonomy, beneficence, and fair distribution of healthcare sources, and how they relate to the use of AI-based devices. The core regulatory guidelines are then viewed in light of these ethical principles. We recommend that a comprehensive regulatory framework with integration of principles of medical ethics be made public. Though no universally accepted framework is available, regulating quality management, risk assessment, and data privacy would help build trust to promote the adoption of AI in healthcare.

Machine learning has transformed ophthalmology, particularly in predictive and discriminatory models for vitreoretinal pathologies. However, generative modeling, especially generative adversarial networks (GANs), remains underexplored. GANs consist of two neural networks-the generator and discriminator-that work in opposition to synthesize highly realistic images. These synthetic images can enhance diagnostic accuracy, expand the capabilities of imaging technologies, and predict treatment responses. GANs have already been applied to fundus imaging, optical coherence tomography (OCT), and fluorescein autofluorescence (FA). Despite their potential, GANs face challenges in reliability and accuracy. This review explores GAN architecture, their advantages over other deep learning models, and their clinical applications in retinal disease diagnosis and treatment monitoring. Furthermore, we discuss the limitations of current GAN models and propose novel applications combining GANs with OCT, OCT-angiography, fluorescein angiography, fundus imaging, electroretinograms, visual fields, and indocyanine green angiography.

An increase in the prevalence of lung cancer that is not smoking-related has been noticed in recent years. Unfortunately, these patients are not included in low dose computer tomography (LDCT) screening programs and are not actually considered in early diagnosis. Therefore, improved early diagnosis methods are urgently needed for non-smokers. It is necessary to establish a prediction model for non-smoking individuals at intermediate to high risk of developing lung cancer (LC) and develop a tool to address the significant gap in evaluating pulmonary nodules in non-smokers.

We retrospectively investigated 1121 patients with pulmonary nodules, who underwent LDCT examinations between September 2019 and March 2023. Five artificial intelligence (AI) algorithms were used to build two kinds of models and identify which one was better at diagnosing non-smoking pulmonary nodules patients. In the first model, we assigned 554 non-smoking individuals to a training cohort and 150 non-smoking patients to an independent validation cohort. The second model included 971 patients for the training set and 150 non-smoking patients for an independent validation set. All LDCT images of participants were obtained for AI analysis. AI of LDCT scans, liquid biopsy, and clinical characteristics were collected for model building.

Among LC patients, 58,4% were non-smokers. Non-smoking patients had a high incidence of LC (71.4%), and women showed a significant excess risk compared with non-smoking men in terms of LC risk. Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99).

These results could significantly improve early non-smoker LC diagnosis and treatment for non-smoker patients with malignant nodules. The established multi-omics model is a noninvasive prediction tool for non-smoking malignant pulmonary nodule diagnosis. Validation revealed that these models exhibited excellent discrimination and calibration capacities, especially the first model built using the RF method, suggesting their clinical utility in the early screening and diagnosis of non-smoking LC.

Background/Objectives: Artificial intelligence (AI) is gaining an increasing amount of influence in various fields, including medicine. In radiology, where diagnoses are based on collaboration between diagnostic devices and the professional experience of radiologists, AI intervention seems much easier than in other fields, but this is often not the case. Many times, the patients orient themselves according to the doctor, which is not applicable in the case of AI. Another limitation rests in the controversies regarding medico-legal liability. In the field of radio-imaging in Romania, the implementation of AI systems in diagnosis is at its beginning. An important aspect of this is raising awareness among the population about these assistive AI systems and, also, awareness of the technological evolution of AI among medical staff. This narrative review manuscript analyzes the existing literature data regarding the medico-legal aspects of AI application in radiology, highlighting the controversial aspects and the lack of statutory legislative regulations in Romania. Methods: A detailed search was conducted across three electronic databases including MEDLINE/PubMed, Scopus, and Web of Science, with 53 papers serving as the literature corpus of our review. Results: General requirements for artificial intelligence systems used in radiology have been established. In the radiological diagnostic process, there are five levels of AI system implication. Until now, completely autonomous AI systems have not been used. Regarding liability in the case of malpractice, at the currently accepted legislative level, most of the time, the radiologist is liable for their own fault or non-compliant use of diagnostic AI systems. Controversies arise in the case of radio-imaging diagnosis in which AI systems act autonomously. Conclusions: In order for AI diagnostic radio-imaging systems to be implemented, they must meet certain quality standards and be approved. The radiologist must know these systems, accept them, know their limits, and validate them in accordance with their degree of involvement in radiological diagnosis. Considering the evolution of technology in the Romanian medical system, including radiology, in the future, an alignment with the legal standards established/proposed at the European level is desired.

Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation of medical AI is the remaining uncertainty regarding requirements for AI users at the bedside. An overview of the academic literature on human requirements for the adequate use of AI in clinical settings is therefore of significant value.

A systematic review of the potential implications of medical AI for the required competencies of physicians as mentioned in the academic literature.

Our findings emphasize the importance of physicians' critical human skills, alongside the growing demand for technical and digital competencies.

Concrete guidance on physicians' required competencies in AI-assisted clinical settings remains ambiguous and requires further clarification and specification. Dissensus remains over whether physicians are adequately equipped to use and monitor AI in clinical settings in terms of competencies, skills and expertise, issues of ownership regarding normative guidance, and training of physicians' skills.

Our review offers a basis for subsequent further research and normative analysis on the responsible use of AI in clinical settings.

Future research should clearly outline (i) how physicians must be(come) competent in working with AI in clinical settings, (ii) who or what should take ownership of embedding these competencies in a normative and regulatory framework, (iii) investigate conditions for achieving a reasonable amount of trust in AI, and (iv) assess the connection between trust and efficiency in patient care.

Artificial intelligence (AI) has transformed healthcare, yet patients' acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients' resistance to AI. This study explores the variables influencing patients' resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI's promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI's efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare.

Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.

A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated.

Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs.

The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

The rapid advancement of generative artificial intelligence (AI), exemplified by tools like ChatGPT, has transformed the healthcare landscape, particularly in patient education and the patient-physician relationship. While AI in healthcare has traditionally focused on data analysis and predictive analytics, the rise of generative AI has introduced new opportunities and challenges in patient interactions, information dissemination, and the overall dynamics of patient care. This narrative review explores the dual impact of generative AI on healthcare, examining its role in enhancing patients' understanding of medical conditions, promoting self-care, and supporting healthcare decision-making. Additionally, the review considers the potential risks, such as the erosion of trust in the patient-physician relationship and the spread of misinformation, while addressing ethical implications and the future integration into clinical practice. A comprehensive literature search, conducted using databases like PubMed, MEDLINE, Scopus, and Google Scholar, included studies published between 2010 and 2024 that discussed the role of generative AI in patient education, engagement, and the patient-physician relationship. Findings show that generative AI tools significantly enhance patient health literacy by making complex medical information more accessible, personalized, and interactive, thus empowering patients to take a more active role in managing their healthcare. However, risks such as misinformation and the undermining of the patient-physician relationship were also identified, with case studies highlighting both positive and negative outcomes. To fully harness the potential of AI in healthcare, it is essential to integrate these tools thoughtfully, ensuring they complement rather than replace the personalized care provided by physicians. Future research should focus on addressing ethical challenges and optimizing AI's role in clinical practice to maintain trust, communication, and the quality of patient care.

To obtain detailed data on the acceptance of an artificial intelligence chatbot (ChatGPT; OpenAI, San Francisco, CA, USA) in ophthalmology among physicians, a survey explored physician responses regarding using ChatGPT in ophthalmology. The survey included questions about the applications of ChatGPT in ophthalmology, future concerns such as job replacement or automation, research, medical education, patient education, ethical concerns, and implementation in practice. One hundred ninety-nine ophthalmic surgeons participated in this study. Approximately two-thirds of the participants had 15 years or more experience in ophthalmology. One hundred sixteen reported that they had used ChatGPT. We found no difference in age, gender, or level of experience between those who used or did not use ChatGPT. ChatGPT users tend to consider ChatGPT and artificial intelligence (AI) as useful in ophthalmology (P=0.001). Both users and non-users think that AI is useful for identifying early signs of eye disease, providing decision support in treatment planning, monitoring patient progress, answering patient questions, and scheduling appointments. Both users and non-users believe there are some issues related to the use of AI in health care, such as liability issues, privacy concerns, accuracy of diagnosis, trust of the chatbot, ethical issues, and information bias. The use of ChatGPT and other forms of AI is increasingly becoming accepted among ophthalmologists. AI is seen as a helpful tool for improving patient education, decision support, and medical services, but there are also concerns regarding privacy and job displacement, which warrant human oversight.

Artificial Intelligence (AI) beginning to integrate in healthcare, is ushering in a transformative era, impacting diagnostics, altering personalized treatment, and significantly improving operational efficiency. The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. The goal of this review is to present a thorough analysis of AI's effects on healthcare while providing stakeholders with a road map for navigating this changing environment.

This review analyzes the impact of AI on healthcare using data from the Web of Science (2014-2024), focusing on keywords like AI, ML, and healthcare applications. It examines the uses and effects of AI on healthcare by synthesizing recent literature and real-world case studies, such as Google Health and IBM Watson Health, highlighting AI technologies, their useful applications, and the difficulties in putting them into practice, including problems with data security and resource limitations. The review also discusses new developments in AI, and how they can affect society.

The findings demonstrate how AI is enhancing the skills of medical professionals, enhancing diagnosis, and opening the door to more individualized treatment plans, as reflected in the steady rise of AI-related healthcare publications from 158 articles (3.54%) in 2014 to 731 articles (16.33%) by 2024. Core applications like remote monitoring and predictive analytics improve operational effectiveness and patient involvement. However, there are major obstacles to the mainstream implementation of AI in healthcare, including issues with data security and budget constraints.

Healthcare may be transformed by AI, but its successful use requires ethical and responsible use. To meet the changing demands of the healthcare sector and guarantee the responsible application of AI technologies, the evaluation highlights the necessity of ongoing research, instruction, and multidisciplinary cooperation. In the future, integrating AI responsibly will be essential to optimizing its advantages and reducing related dangers.

Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.

AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.

• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.

• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries.

Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems.

The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.

A systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.

100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: "Prediction of diseases, their diagnosis, and outcomes," "Prediction of organizational issues and attributes," "Prediction of mental health issues and attributes," "Prediction of polypharmacy and emotional analysis of texts," "Prediction of climate change issues and attributes," and "Prediction and identification of success and satisfaction among healthcare individuals."

The findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.

Thumb carpometacarpal (CMC) joint osteoarthritis is among the most common degenerative hand diseases. Thumb CMC arthroplasty, or trapeziectomy with or without tendon augmentation, is the most frequently performed surgical treatment and has a strong safety profile. Though adverse outcomes are infrequent, the ability to predict risk for complications has substantial clinical benefits. In the present study, we evaluated a well-known surgical database with machine learning (ML) techniques to predict short-term complications and reoperations after thumb CMC arthroplasty.

A retrospective study was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005-2020. Outcomes were 30-day wound and medical complications and 30-day return to the operating room. We used three ML algorithms - a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tree (XGBoost), and a deep learning Neural Network (NN). Feature importance analysis was performed in the highest performing model for each outcome to identify predictors with the greatest contributions.

We included a total of 7711 cases. The RF was the best performing algorithm for all outcomes, with an AUC score of 0.61±0.03 for reoperations, 0.55±0.04 for medical complications, and 0.59±0.03 for wound complications. On feature importance analysis, procedure duration was the highest weighted predictor for reoperations. In all outcomes, procedure duration, older age, and female sex were consistently among the top five predictors.

We successfully developed ML algorithms to predict reoperations, wound complications, and medical complications. RF models had the highest performance in all outcomes.

The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal dialysis (PD) presents transformative opportunities for optimizing treatment outcomes and informing clinical decision-making. This study aims to provide a comprehensive overview of the applications of AI/ML techniques in PD, focusing on their potential to predict clinical outcomes and enhance patient care.

This systematic review was conducted according to PRISMA guidelines (2020), searching key databases for articles on AI and ML applications in PD. The inclusion criteria were stringent, ensuring the selection of high-quality studies. The search strategy comprised MeSH terms and keywords related to PD, AI, and ML. 793 articles were identified, with nine ultimately meeting the inclusion criteria. The review utilized a narrative synthesis approach to summarize findings due to anticipated study heterogeneity.

Nine studies met the inclusion criteria. The studies varied in sample size and employed diverse AI and ML techniques, reflecting the breadth of data considered. Mortality prediction emerged as a recurrent theme, demonstrating the significance of AI and ML in prognostic accuracy. Predictive modeling extended to technique failure, hospital stay prediction, and pathogen-specific immune responses, showcasing the versatility of AI and ML applications in PD.

This systematic review highlights the diverse applications of AI/ML in peritoneal dialysis, demonstrating their potential to enhance predictive accuracy, risk stratification, and decision support. However, limitations such as small sample sizes, single-center studies, and potential biases warrant further research and external validation. Future perspectives include integrating these AI/ML models into routine clinical practice and exploring additional use cases to improve patient outcomes and healthcare decision-making in PD.

To evaluate the effectiveness of ChatGPT in providing insights into common urinary incontinence concerns within urogynecology. By analyzing the model's responses against established benchmarks of accuracy, completeness, and safety, the study aimed to quantify its usefulness for informing patients and aiding healthcare providers.

An expert-driven questionnaire was developed, inviting urogynecologists worldwide to assess ChatGPT's answers to 10 carefully selected questions on urinary incontinence (UI). These assessments focused on the accuracy of the responses, their comprehensiveness, and whether they raised any safety issues. Subsequent statistical analyses determined the average consensus among experts and identified the proportion of responses receiving favorable evaluations (a score of 4 or higher).

Of 50 urogynecologists that were approached worldwide, 37 responded, offering insights into ChatGPT's responses on UI. The overall feedback averaged a score of 4.0, indicating a positive acceptance. Accuracy scores averaged 3.9 with 71% rated favorably, whereas comprehensiveness scored slightly higher at 4 with 74% favorable ratings. Safety assessments also averaged 4 with 74% favorable responses.

This investigation underlines ChatGPT's favorable performance across the evaluated domains of accuracy, comprehensiveness, and safety within the context of UI queries. However, despite this broadly positive reception, the study also signals a clear avenue for improvement, particularly in the precision of the provided information. Refining ChatGPT's accuracy and ensuring the delivery of more pinpointed responses are essential steps forward, aiming to bolster its utility as a comprehensive educational resource for patients and a supportive tool for healthcare practitioners.

This study explored student pharmacists' perceptions and attitudes regarding artificial intelligence (AI) and machine learning (ML) in pharmacy practice. Due to AI/ML's promising prospects, understanding students' current awareness, comprehension, and hopes for their use in this field is essential.

In April 2024, a Zoom focus group discussion was conducted with 6 student pharmacists using a self-developed interview guide. The guide included questions about the benefits, challenges, and ethical considerations of implementing AI/ML in pharmacy practice and education. The participants' demographic information was collected through a questionnaire. The research team conducted a thematic analysis of the discussion transcript. The results generated by a team member using NVivo were compared with those generated by ChatGPT, and all discrepancies were addressed.

Student pharmacists displayed a generally positive attitude toward the implementation of AI/ML in pharmacy practice but lacked knowledge about AI/ML applications. Participants recognized several advantages of AI/ML implementation in pharmacy practice, including improved accuracy and time-saving for pharmacists. Some identified challenges were alert fatigue, AI/ML-generated errors, and the potential obstacle to person-centered care. The study participants expressed their interest in learning about AI/ML and their desire to integrate these technologies into pharmacy education.

The demand for integrating AI/ML into pharmacy practice is increasing. Student and professional pharmacists need additional AI/ML training to equip them with knowledge and practical skills. Collaboration between pharmacists, institutions, and AI/ML companies is essential to address barriers and advance AI/ML implementation in the pharmacy field.

Recent advancements in artificial intelligence (AI) have shown significant potential in the medical field, although many applications are still in the research phase. This paper provides a comprehensive review of advancements in augmented reality (AR), mixed reality (MR), and virtual reality (VR) for surgical applications from 2019 to 2024 to accelerate the transition of AI from the research to the clinical phase. This paper also provides an overview of proposed databases for further use in extended reality (XR), which includes AR, MR, and VR, as well as a summary of typical research applications involving XR in surgical practices. Additionally, this paper concludes by discussing challenges and proposed solutions for the application of XR in the medical field. Although the areas of focus and specific implementations vary among AR, MR, and VR, current trends in XR focus mainly on reducing workload and minimizing surgical errors through navigation, training, and machine learning-based visualization. Through analyzing these trends, AR and MR have greater advantages for intraoperative surgical functions, whereas VR is limited to preoperative training and surgical preparation. VR faces additional limitations, and its use has been reduced in research since the first applications of XR, which likely suggests the same will happen with further development. Nonetheless, with increased access to technology and the ability to overcome the black box problem, XR's applications in medical fields and surgery will increase to guarantee further accuracy and precision while reducing risk and workload.

Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance.

This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare.

Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional.

The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.

In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.

Large language models (LLMs) have dominated public interest due to their apparent capability to accurately replicate learned knowledge in narrative text. However, there is a lack of clarity about the accuracy and capability standards of LLMs in health care examinations.

We conducted a systematic review of LLM accuracy, as tested under health care examination conditions, as compared to known human performance standards.

We quantified the accuracy of LLMs in responding to health care examination questions and evaluated the consistency and quality of study reporting. The search included all papers up until September 10, 2023, with all LLMs published in English journals that report clear LLM accuracy standards. The exclusion criteria were as follows: the assessment was not a health care exam, there was no LLM, there was no evaluation of comparable success accuracy, and the literature was not original research.The literature search included the following Medical Subject Headings (MeSH) terms used in all possible combinations: "artificial intelligence," "ChatGPT," "GPT," "LLM," "large language model," "machine learning," "neural network," "Generative Pre-trained Transformer," "Generative Transformer," "Generative Language Model," "Generative Model," "medical exam," "healthcare exam," and "clinical exam." Sensitivity, accuracy, and precision data were extracted, including relevant CIs.

The search identified 1673 relevant citations. After removing duplicate results, 1268 (75.8%) papers were screened for titles and abstracts, and 32 (2.5%) studies were included for full-text review. Our meta-analysis suggested that LLMs are able to perform with an overall medical examination accuracy of 0.61 (CI 0.58-0.64) and a United States Medical Licensing Examination (USMLE) accuracy of 0.51 (CI 0.46-0.56), while Chat Generative Pretrained Transformer (ChatGPT) can perform with an overall medical examination accuracy of 0.64 (CI 0.6-0.67).

LLMs offer promise to remediate health care demand and staffing challenges by providing accurate and efficient context-specific information to critical decision makers. For policy and deployment decisions about LLMs to advance health care, we proposed a new framework called RUBRICC (Regulatory, Usability, Bias, Reliability [Evidence and Safety], Interoperability, Cost, and Codesign-Patient and Public Involvement and Engagement [PPIE]). This presents a valuable opportunity to direct the clinical commissioning of new LLM capabilities into health services, while respecting patient safety considerations.

OSF Registries osf.io/xqzkw; https://osf.io/xqzkw.

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions.

Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection.

The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection.

The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Artificial intelligence (AI) technologies (natural language processing (NLP), speech recognition (SR), and machine learning (ML)) can transform clinical documentation in healthcare. This scoping review evaluates the impact of AI on the accuracy and efficiency of clinical documentation across various clinical settings (hospital wards, emergency departments, and outpatient clinics). We found 176 articles by applying a specific search string on Ovid. To ensure a more comprehensive search process, we also performed manual searches on PubMed and BMJ, examining any relevant references we encountered. In this way, we were able to add 46 more articles, resulting in 222 articles in total. After removing duplicates, 208 articles were screened. This led to the inclusion of 36 studies. We were mostly interested in articles discussing the impact of AI technologies, such as NLP, ML, and SR, and their accuracy and efficiency in clinical documentation. To ensure that our research reflected recent work, we focused our efforts on studies published in 2019 and beyond. This criterion was pilot-tested beforehand and necessary adjustments were made. After comparing screened articles independently, we ensured inter-rater reliability (Cohen's kappa=1.0), and data extraction was completed on these 36 articles. We conducted this study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This scoping review shows improvements in clinical documentation using AI technologies, with an emphasis on accuracy and efficiency. There was a reduction in clinician workload, with the streamlining of the documentation processes. Subsequently, doctors also had more time for patient care. However, these articles also raised various challenges surrounding the use of AI in clinical settings. These challenges included the management of errors, legal liability, and integration of AI with electronic health records (EHRs). There were also some ethical concerns regarding the use of AI with patient data. AI shows massive potential for improving the day-to-day work life of doctors across various clinical settings. However, more research is needed to address the many challenges associated with its use. Studies demonstrate improved accuracy and efficiency in clinical documentation with the use of AI. With better regulatory frameworks, implementation, and research, AI can significantly reduce the burden placed on doctors by documentation.

Background Artificial intelligence (AI) is rapidly evolving within healthcare, promising improvements in patient care, diagnostic accuracy, and therapeutic interventions. As AI technology becomes more integrated into clinical workflows, understanding healthcare professionals' (HCPs) knowledge, attitudes, and practices concerning AI is crucial, particularly in diverse healthcare environments like South Gujarat. This study evaluates HCPs' understanding, perception, and application of AI at a tertiary care teaching hospital in this region. Methods This observational, cross-sectional study utilized a non-validated, structured questionnaire based on the Knowledge, Attitude, and Practice (KAP) framework. A convenient sampling technique was employed to recruit 290 HCPs, including consultant doctors, medical faculty, residents, and interns. Data were collected electronically via Google Forms and analyzed using descriptive statistics. Results Most participants (176; 60.7%) were junior residents, with notable representation from departments like Pharmacology and Community Medicine. Regarding AI knowledge, 80 (27.6%) of participants reported full awareness, while 182 (62.8%) were partially aware. AI subtype knowledge varied, with 84 (28.9%) identifying "Self-awareness" and 50 (17.2%) "Limited Memory." Internet sources were the predominant information source for 171 (58.9%) of participants. Notably, 192 (66.2%) recognized AI's role in saving time and enhancing accuracy, although some expressed concerns about its lack of empathy and ethical implications. Conclusions The findings highlight substantial awareness but varying depths of understanding of AI among HCPs, who are interested in further AI education. Increased educational programs on AI's ethical and practical aspects may enhance AI integration into clinical practice, aiding responsible adoption in healthcare settings.

Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.

A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.

The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.

The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.