With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI in medical education, providing guidance for educators, and overcoming challenges in the implementation process is particularly important.

The objective of this study is to explore the current state of AI applications in medical education.

A systematic search was conducted across databases such as PsycINFO, CINAHL, Scopus, PubMed, and Web of Science to identify relevant studies. The Critical Appraisal Skills Programme (CASP) was employed for the quality assessment of these studies, followed by thematic synthesis to analyze the themes from the included research.

Ultimately, 21 studies were identified, establishing four themes: (1) Shaping the Future: Current Trends in AI within Medical Education; (2) Advancing Medical Instruction: The Transformative Power of AI; (3) Navigating the Ethical Landscape of AI in Medical Education; (4) Fostering Synergy: Integrating Artificial Intelligence in Medical Curriculum.

Artificial intelligence's role in medical education, while not yet extensive, is impactful and promising. Despite challenges, including ethical concerns over privacy, responsibility, and humanistic care, future efforts should focus on integrating AI through targeted courses to improve educational quality.

Telemedicine is a key factor in increasing patient accessibility, satisfaction with treatment, and quality of care, effectively utilizing physicians' time, and improving communication among medical experts. Despite global interest in telemedicine, there is limited research exploring users' perspectives on telemedicine within the context of Egyptian university hospitals. This study aims to examine physicians' and employees' levels of knowledge, attitudes, and practices toward telemedicine.

A cross-sectional study was conducted at Tanta University's medical campus from November 2023 to March 2024. One-thousand employees and physicians were surveyed. A self-administered questionnaire was used to collect the data. It consists of four sections: sociodemographic data, knowledge about telemedicine, attitude, and practice of telemedicine.

Half of physicians used telemedicine, and 38.2% of the employees have used e-health services. Applications of telemedicine included patients' investigations communicated through the Internet (76.4%), patients' management with drugs (71.4%), direct medical consultation between patient and physician (65.4%), second opinion consulting (57.6%), sharing experiences and new trends in medicine and surgery with other specialists in other countries (54%), and follow-up of patients through the electronic technologies (53%). About three-quarters of physicians and employees had a positive attitude toward telemedicine. The advantages reported include being easy to use (63%), reducing travel costs for patients (68.6%), and its importance during pandemics, e.g., COVID-19 (59.8%). However, our results indicated potential barriers when using telemedicine, including the need for training; elderly patients find difficulty dealing with technology, poor infrastructure, technical issues, difficulty for patients to express their feelings and communicate with physicians, and a lack of body language.

A considerable percentage of participants were already using telemedicine services, and they were satisfied with the telemedicine system. Though most participants had favorable attitudes toward telemedicine, potential barriers were reported, such as training for physicians and patients, difficulty dealing with technology, poor infrastructure, and technical issues. These findings underscore the need to develop and implement a regulatory framework that supports telemedicine adoption, including data protection, patient confidentiality, and reimbursement standards.

The global incidence of myopia is increasing, and high myopia increases the risk of pathological myopia, which can lead to irreversible visual impairment, posing a significant global health concern. Artificial intelligence (AI) may be a solution to the myopia pandemic, with potential applications in early identification, risk stratification, progression prediction, and timely intervention to address unmet needs. AI has been developed to detect, diagnose, and predict the progression of myopia in both children and adults. In this review, the current state of AI technology applications in the field of myopia has been comprehensively reviewed, and the challenges, current development status, and future directions of AI have also been discussed, which hold great significance for the further application of AI in myopia management.

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.

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.

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.

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.

Artificial intelligence (AI) has revolutionized the field of gastroenterology, particularly in endoscopic and colonoscopic procedures. These AI technologies aim to enhance diagnostic accuracy by facilitating the detection of gastrointestinal lesions, such as polyps and neoplasms. However, the rapid expansion of research in this area necessitates a comprehensive analysis to assess global trends and contributions. This study aims to conduct a thorough bibliometric and visualization analysis of global research focused on AI applications in endoscopy and colonoscopy.

A systematic search was conducted in September 2024 using the Web of Science Core Collection. The data were analyzed using VOSviewer, CiteSpace, and R software, focusing on co-authorship, co-citation, and keyword trends.

Research output on AI in endoscopy and colonoscopy has seen significant growth since 2016, peaking in 2023 with 345 publications. The top contributing country was China, with 399 publications, while the United States led in centrality with a score of 0.27, indicating its key position in research collaborations. Showa University contributed the highest number of institutional publications (64 papers). Mori Y emerged as the leading author, with 53 publications, reflecting his significant influence in the field. The leading journal was Gastrointestinal Endoscopy, contributing 72 publications and accumulating 6,496 citations. The most frequently occurring keywords were "diagnosis," "classification," and "cancer." The cluster analysis identified key research areas, with newer clusters emerging around "adenoma detection," "polyp segmentation," and "wireless capsule endoscopy." These clusters have shown an increasing trend over the past few years, reflecting the growing focus on using AI to optimize diagnostic procedures in real-time.

The bibliometric analysis highlights the rapid expansion and diversification of AI research in endoscopy and colonoscopy. Key clusters, such as "adenoma detection" and "polyp segmentation," underscore the field's shift toward real-time diagnostic improvements. As AI technologies become more integrated into clinical practice, they are set to improve diagnostic accuracy and patient outcomes in gastroenterology.

➢ Three-dimensional (3D) printing and virtual modeling, using computed tomographic (CT) scans as a base for the 3D-printed model, help surgeons to visualize relevant anatomy, may provide a better understanding of fracture planes, may help to plan surgical approaches, and can possibly simulate surgical fixation options.➢ Navigation systems create real-time 3D maps of patient anatomy intraoperatively, with most literature in orthopaedic trauma thus far demonstrating efficacy in percutaneous screw placement using preoperative imaging data or intraoperative markers.➢ Augmented reality and virtual reality are new applications in orthopaedic trauma, with the former in particular demonstrating the potential utility in intraoperative visualization of implant placement.➢ Use of 3D-printed metal implants has been studied in limited sample sizes thus far. However, early results have suggested that they may have good efficacy in improving intraoperative measures and postoperative outcomes.

Digital health technologies (DHTs) have rapidly advanced in the past two decades, through developments in mobile and wearable devices and most recently with the explosion of artificial intelligence (AI) capabilities and subsequent extension into the health space. DHT has myriad potential applications to deglutology, many of which have undergone promising investigations and developments in recent years. We present the first literature review on applications of DHT in swallowing health, from screening to therapeutics. Public health interventions for swallowing care are increasingly needed in the setting of aging populations in the West and East Asia, and DHT may offer a scalable and low-cost solution.

A narrative review was performed using PubMed and Google Scholar to identify recent research on applications of AI and digital health in swallow practice. Database searches, conducted in September 2024, included terms such as "digital," "AI," "machine learning," "tools" in combination with "deglutition," "Otolaryngology," "Head and Neck," "speech language pathology," "swallow," and "dysphagia." Primary literature pertaining to digital health in deglutology was included for review.

We review the various applications of DHT in swallowing care, including prevention, screening, diagnosis, treatment planning and rehabilitation.

DHT may offer innovative and scalable solutions for swallowing care as public health needs grow and in the setting of limited specialized healthcare workforce. These technological advances are also being explored as time and resource saving solutions at many points of care in swallow practice. DHT could bring affordable and accurate information for self-management of dysphagia to broader patient populations that otherwise lack access to expert providers.

This study evaluates the role of ChatGPT-4 and Claude 3.5 Sonet in postoperative management for patients undergoing posterior cervical fusion. It focuses on their ability to provide accurate, clear, and relevant responses to patient concerns, highlighting their potential as supplementary tools in surgical aftercare.

Ten common postoperative questions were selected and posed to ChatGPT-4 and Claude 3.5 Sonet. Ten independent neurosurgeons evaluated responses using a structured framework that assessed accuracy, response time, clarity, and relevance. A 5-point Likert scale also measured satisfaction, quality, performance, and importance. Advanced statistical analyses were used to compare the 2 artificial intelligence platforms, including sensitivity, specificity, P values, confidence intervals, and Cohen's d.

Claude 3.5 Sonet outperformed ChatGPT-4 across all metrics, particularly in accuracy (96.5% vs. 80.6%), response time (92.9% vs. 76.4%), clarity (94.6% vs. 75.4%), and relevance (95.5% vs. 74.0%). Likert scale evaluations showed significant differences (P < 0.001) in satisfaction, quality, and performance, with Claude achieving higher ratings. Statistical analyses confirmed large effect sizes, high inter-rater reliability (kappa: 0.85-0.92 for Claude), and narrower confidence intervals, reinforcing Claude's consistency and superior performance.

Claude 3.5 Sonet demonstrated exceptional capability in addressing postoperative concerns for posterior cervical fusion patients, surpassing ChatGPT-4 in accuracy, clarity, and practical relevance. These findings underscore its potential as a reliable artificial intelligence tool for enhancing patient care and satisfaction in surgical aftercare.

This paper explores how artificial intelligence (AI) is being woven into precision medicine for neuro-oncology, highlighting its ethical, clinical, and nursing implications in the realm of immunotherapy. With AI-powered diagnostics and predictive analytics, we're seeing a boost in treatment accuracy, which paves the way for more personalized and effective care. On the clinical side, AI is fine-tuning targeted therapies, leading to better patient outcomes and less treatment-related toxicity. However, ethical concerns pop up around data privacy, algorithmic bias, and fair access to these AI-driven treatments. Nurses are at the forefront of tackling these issues, ensuring that care remains patient-centered, monitoring AI-assisted interventions, and grappling with ethical challenges. Their role in education and advocacy is crucial in connecting the dots between AI innovations and compassionate care. As AI continues to advance, it's vital for different disciplines to work together to tap into its potential while maintaining ethical standards and enhancing care in neuro-oncology.

Artificial intelligence (AI) is transforming healthcare by enhancing diagnostics, personalizing medicine and improving surgical precision. However, its integration into healthcare systems raises significant ethical and legal challenges. This review explores key ethical principles-autonomy, beneficence, non-maleficence, justice, transparency and accountability-highlighting their relevance in AI-driven decision-making. Legal challenges, including data privacy and security, liability for AI errors, regulatory approval processes, intellectual property and cross-border regulations, are also addressed. As AI systems become increasingly autonomous, questions of responsibility and fairness must be carefully considered, particularly with the potential for biased algorithms to amplify healthcare disparities. This paper underscores the importance of multi-disciplinary collaboration between technologists, healthcare providers, legal experts and policymakers to create adaptive, globally harmonized frameworks. Public engagement is emphasized as essential for fostering trust and ensuring ethical AI adoption. With AI technologies advancing rapidly, a flexible regulatory environment that evolves with innovation is critical. Aligning AI innovation with ethical and legal imperatives will lead to a safer, more equitable healthcare system for all.

Artificial intelligence (AI) could improve accuracy and efficiency of breast cancer screening. However, many women distrust AI in health care, potentially jeopardizing breast cancer screening participation rates. The aim was to quantify community preferences for models of AI implementation within breast cancer screening.

An online discrete choice experiment survey of people eligible for breast cancer screening aged 40 to 74 years in Australia. Respondents answered 10 questions where they chose between two screening options created by an experimental design. Each screening option described the role of AI (supplementing current practice, replacing one radiologist, replacing both radiologists, or triaging), and the AI accuracy, ownership, representativeness, privacy, and waiting time. Analysis included conditional and latent class models, willingness-to-pay, and predicted screening uptake.

The 802 participants preferred screening where AI was more accurate, Australian owned, more representative and had shorter waiting time for results (all p < .001). There were strong preferences (p < .001) against AI alone or as triage. Three patterns of preferences emerged: positive about AI if accuracy improves (40% of sample), strongly against AI (42%), and concerned about AI (18%). Participants were willing to accept AI replacing one human reader if their results were available 10 days faster than current practice but would need results 21 days faster for AI as triage. Implementing AI inconsistent with community preferences could reduce participation by up to 22%.

With digitalization in the field of healthcare, using patient image based data, there is also increasing concerns on protection of patient privacy. Globally various legal rules and regulations have been adopted for stringent measures on data privacy. However, despite the growing importance of privacy, there are currently no universally defined protocols outlining the specific parameters for the de-identification/pseudo-anonymization of medical images.

The study aims to assess current methods for protecting patient privacy in medical image datasets used in research and healthcare technology development.

A comprehensive, systematic search was conducted with a defined search string across databases, including PubMed/Medline, Scopus, Web of Science, Embase, and Google Scholar. Studies were selected based on their focus on the procedures used for anonymization, pseudo-anonymization, and de-identification of medical images during the creation of datasets.

From an initial pool of 324 potentially relevant articles, 13 studies were ultimately included in the final review after meeting the inclusion criteria. Of these, the majority focused on open-source datasets, which are accessible for use in research and algorithm development. Methods of de-identification of images included burn-in annotation, defacing processes, removal of DICOM tags, and facial de-identification. A medical image protection checklist was created based on the findings of our review.

The review explores techniques such as removal or masking of personal identifiers, DICOM tag removal, facial de-identification GOAL: The insights gathered aim to help develop standardized privacy protocols to be adhered by healthcare professionals for responsible use of medical imaging data, ensuring the responsible use of medical imaging data for healthcare advancements.

The findings of this review highlight several key considerations for effective pseudo-anonymization and de-identification of medical images. The review emphasizes the need for a careful balance between protecting patient privacy and ensuring that medical datasets retain sufficient quality and richness for research and technological development.

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

The increasing adoption of artificial intelligence (AI) in medicines, pharmacotherapy, and pharmaceutical services necessitates clear guidance on reporting standards. While the MedinAI Statement (Bottacin in Int J Clin Pharm, https://doi.org/10.1007/s11096-025-01905-3, 2025) provides core guidelines for reporting AI studies in these fields, detailed explanations and practical examples are crucial for optimal implementation. This companion document was developed to offer comprehensive guidance and real-world examples for each guideline item. The document elaborates on all 14 items and 78 sub-items across four domains: core, ethical considerations in medication and pharmacotherapy, medicines as products, and services related to medicines and pharmacotherapy. Through clear, actionable guidance and diverse examples, this document enhances MedinAI's utility, enabling researchers and stakeholders to improve the quality and transparency of AI research reporting across various contexts, study designs, and development stages.

Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.

We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.

A comprehensive search was conducted using MeSH keywords, including "artificial intelligence," "sexual medicine," "sexual health," and "machine learning." Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with conflicts resolved by a third reviewer. Articles in English language that reported on AI in sexual medicine and health were included. A total of 69 full-text articles were systematically analyzed based on predefined inclusion criteria. Data extraction included information on article characteristics, study design, assessment methods, and outcomes.

The initial search yielded 905 articles relevant to AI in sexual medicine. Upon assessing the full texts of 121 articles for eligibility, 52 studies unrelated to AI in sexual health were excluded, resulting in 69 articles for systematic review. The analysis revealed AI's accuracy in preventing, diagnosing, and decision-making in sexually transmitted diseases. AI also demonstrated the ability to diagnose and offer precise treatment plans for male and female sexual dysfunction and infertility, accurately predict sex from bone and teeth imaging, and correctly predict and diagnose sexual orientation and relationship issues. AI emerged as a promising modality with significant implications for the future of sexual medicine.

Further research is essential to unlock the potential of AI in sexual medicine. AI presents advantages such as accessibility, user-friendliness, confidentiality, and a preferred source of sexual health information. However, it still lags human healthcare providers in terms of compassion and clinical expertise.

This study assesses the accuracy and consistency of a commercially available large language model (LLM) in extracting and interpreting sensitivity and reliability data from entire visual field (VF) test reports for the evaluation of glaucomatous defects. Single-page anonymised VF test reports from 60 eyes of 60 subjects were analysed by an LLM (ChatGPT 4o) across four domains-test reliability, defect type, defect severity and overall diagnosis. The main outcome measures were accuracy of data extraction, interpretation of glaucomatous field defects and diagnostic classification. The LLM displayed 100% accuracy in the extraction of global sensitivity and reliability metrics and in classifying test reliability. It also demonstrated high accuracy (96.7%) in diagnosing whether the VF defect was consistent with a healthy, suspect or glaucomatous eye. The accuracy in correctly defining the type of defect was moderate (73.3%), which only partially improved when provided with a more defined region of interest. The causes of incorrect defect type were mostly attributed to the wrong location, particularly confusing the superior and inferior hemifields. Numerical/text-based data extraction and interpretation was overall notably superior to image-based interpretation of VF defects. This study demonstrates the potential and also limitations of multimodal LLMs in processing multimodal medical investigation data such as VF reports.

Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, remains a leading cause of cardiovascular morbidity and mortality. Artificial intelligence (AI) holds promise for potential improvement of risk stratification, diagnosis, and management of VTE.

This narrative review explores the applications, benefits, and limitations of AI in VTE management. AI models were shown to outperform conventional methods in identifying high-risk candidates for VTE prophylaxis treatments in several postsurgical settings. It has also been demonstrated to be efficient in the early detection of VTE events, particularly through point-of-care AI-guided sonography and computer tomography image processing. Data biases, model transparency, and the need for regulatory frameworks remain significant limitations in the full integration of AI into clinical practice.

AI has the potential to improve VTE care by enhancing risk stratification and diagnosis. The integration of AI-driven models into clinical workflows has the potential to reduce costs, streamline diagnostic processes, and ensure effective management of VTE. Safe and effective integration of AI into VTE care requires addressing its limitations, such as interpretability, privacy, and algorithmic bias.

Background: Large language models (LLMs) have the potential to enhance information processing and clinical reasoning in the healthcare industry but are hindered by inaccuracies and hallucinations. The retrieval-augmented generation (RAG) technique may address these problems by integrating external knowledge sources. Methods: We developed a RAG-based chatbot called Thyro-GenAI by integrating a database of textbooks and guidelines with LLM. Thyro-GenAI and three service LLMs: OpenAI's ChatGPT-4o, Perplexity AI's ChatGPT-4o, and Anthropic's Claude 3.5 Sonnet, were asked personalized clinical questions about thyroid disease. Three thyroid specialists assessed the quality of the generated responses and references without being blinded, which allowed them to interact with different chatbot interfaces. Results: Thyro-GenAI achieved the highest inverse-weighted mean rank for overall response quality. The overall inverse-weighted mean rankings for Thyro-GenAI, ChatGPT, Perplexity, and Claude were 3.0, 2.3, 2.8, and 1.9, respectively. Thyro-GenAI also achieved the second-highest inverse-weighted mean rank for overall reference quality. The overall inverse-weighted mean rankings for Thyro-GenAI, ChatGPT, Perplexity, and Claude were 3.1, 2.3, 3.2, and 1.8, respectively. Conclusions: Thyro-GenAI produced patient-specific clinical reasoning output based on a vector database, with fewer hallucinations and more reliability, compared to service LLMs. This emphasis on evidence-based responses ensures its safety and validity, addressing a critical limitation of existing LLMs. By integrating RAG with LLMs, it has the potential to support frontline clinical decision-making, especially helping first-line physicians by offering reliable decision support while managing thyroid disease patients.

Artificial intelligence is the future of clinical practice and is increasingly utilized in medical management and clinical research. The release of ChatGPT3 in 2022 brought generative AI to the headlines and rekindled public interest in software agents that would complete repetitive tasks and save time. Artificial intelligence/machine learning underlies applications and devices which are assisting clinicians in the diagnosis, monitoring, formulation of prognosis, and treatment of patients with a spectrum of neuromuscular diseases. However, these applications have remained in the research sphere, and neurologists as a specialty are running the risk of falling behind other clinical specialties which are quicker to embrace these new technologies. While there are many comprehensive reviews on the use of artificial intelligence/machine learning in medicine, our aim is to provide a simple and practical primer to educate clinicians on the basics of machine learning. This will help clinicians specializing in neuromuscular and electrodiagnostic medicine to understand machine learning applications in nerve and muscle ultrasound, MRI imaging, electrical impendence myography, nerve conductions and electromyography and clinical cohort studies, and the limitations, pitfalls, regulatory and ethical concerns, and future directions. The question is not whether artificial intelligence/machine learning will change clinical practice, but when and how. How future neurologists will look back upon this period of transition will be determined not by how much changed or by how fast clinicians embraced this change but by how much patient outcomes were improved.

Generative artificial intelligence (AI) may revolutionize health care, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises ethical concerns related to data integrity, patient safety, and appropriate oversight. One of the primary ethical challenges lies in generative AI's potential to produce misleading or fabricated information, posing risks of misdiagnosis or inappropriate treatment recommendations, which underscore the necessity for robust physician oversight. Transparency also remains a critical concern, as the closed-source nature of many large-language models prevents both patients and health care providers from understanding the reasoning behind AI-generated outputs, potentially eroding trust. The lack of regulatory approval for AI as a medical device, combined with concerns around the security of patient-derived data and AI-generated synthetic data, further complicates its safe integration into clinical workflows. Furthermore, synthetic datasets generated by AI, although valuable for augmenting research in areas with scarce data, complicate questions of data ownership, patient consent, and scientific validity. In addition, generative AI's ability to streamline administrative tasks risks depersonalizing care, further distancing providers from patients. These challenges compound the deeper issues plaguing the health care system, including the emphasis of volume and speed over value and expertise. The use of generative AI in medicine brings about mass scaling of synthetic information, thereby necessitating careful adoption to protect patient care and medical advancement. Given these considerations, generative AI applications warrant regulatory and critical scrutiny. Key starting points include establishing strict standards for data security and transparency, implementing oversight akin to institutional review boards to govern data usage, and developing interdisciplinary guidelines that involve developers, clinicians, and ethicists. By addressing these concerns, we can better align generative AI adoption with the core foundations of humanistic health care, preserving patient safety, autonomy, and trust while harnessing AI's transformative potential. LEVEL OF EVIDENCE: Level V, expert opinion.

This article explores the potential impact of OpenAI's Sora, a generative video modeling technology, on neurosurgical training. It evaluates how such technology could revolutionize the field by providing realistic surgical simulations, thereby enhancing the learning experience and proficiency in complex procedures for neurosurgical trainees.

The study examines the incorporation of this technology into neurosurgical education by leveraging transformer architecture and processing of video and image data. It involves compiling a neurosurgical procedure dataset for model training, aiming to create accurate, high-fidelity simulations.

Our findings indicate significant potential applications in neurosurgical training, including immersive simulations for skill development and exposure to diverse surgical scenarios. The technology also promises to transform assessment and feedback, introducing a standardized, objective way to measure and improve trainee competencies.

Integrating generative video modeling technology into neurosurgical education marks a progressive step toward enhancing training methodologies. Despite challenges in technical, ethical, and practical domains, continuous development and evaluation could lead to substantial advancements in surgical education, preparing neurosurgeons more effectively for their demanding roles.

Large language models (LLMs) have emerged as powerful tools in many fields, including clinical pharmacology and translational medicine. This paper aims to provide a comprehensive primer on the applications of LLMs to these disciplines. We will explore the fundamental concepts of LLMs, their potential applications in drug discovery and development processes ranging from facilitating target identification to aiding preclinical research and clinical trial analysis, and practical use cases such as assisting with medical writing and accelerating analytical workflows in quantitative clinical pharmacology. By the end of this paper, clinical pharmacologists and translational scientists will have a clearer understanding of how to leverage LLMs to enhance their research and development efforts.

Radiology is one of the medical specialties most significantly impacted by Artificial Intelligence (AI). AI systems, particularly those employing machine and deep learning, excel in processing large datasets and comparing images from similar contexts, fulfilling radiological demands. However, the implementation of AI in radiology presents notable challenges, including concerns about data privacy, informed consent, and the potential for external interferences affecting decision-making processes. Biases represent another critical issue, often stemming from unrepresentative datasets or inadequate system training, which can lead to distorted outcomes and exacerbate healthcare inequalities. Additionally, generative AI systems may produce 'hallucinations' arising from their reliance on probabilistic modeling without the ability to distinguish between true and false information. Such risks raise ethical and legal questions, especially when AI-induced errors harm patient health. Concerning liability for medical errors involving AI, healthcare professionals currently retain full accountability for their decisions. AI systems remain tools to support, not replace, human expertise and judgment. Nevertheless, the "black box" nature of many AI models - wherein the reasoning behind outputs remains opaque - limits the possibility of fully informed consent. We advocate for prioritizing Explainable Artificial Intelligence (XAI) in radiology. While potentially less performant than black-box models, XAI enhances transparency, allowing patients to understand how their data is used and how AI influences clinical decisions, aligning with ethical standards.

Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL's applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.

The use of large language models (LLMs) is growing for both clinicians and patients. While researchers and clinicians have explored LLMs to manage patient portal messages and reduce burnout, there is less documentation about how patients use these tools to understand clinical notes and inform decision-making. This proof-of-concept study examined the reliability and accuracy of LLMs in responding to patient queries based on an open visit note.

In a cross-sectional proof-of-concept study, 3 commercially available LLMs (ChatGPT 4o, Claude 3 Opus, Gemini 1.5) were evaluated using 4 distinct prompt series-Standard, Randomized, Persona, and Randomized Persona-with multiple questions, designed by patients, in response to a single neuro-oncology progress note. LLM responses were scored by the note author (neuro-oncologist) and a patient who receives care from the note author, using an 8-criterion rubric that assessed Accuracy, Relevance, Clarity, Actionability, Empathy/Tone, Completeness, Evidence, and Consistency. Descriptive statistics were used to summarize the performance of each LLM across all prompts.

Overall, the Standard and Persona-based prompt series yielded the best results across all criterion regardless of LLM. Chat-GPT 4o using Persona-based prompts scored highest in all categories. All LLMs scored low in the use of Evidence.

This proof-of-concept study highlighted the potential for LLMs to assist patients in interpreting open notes. The most effective LLM responses were achieved by applying Persona-style prompts to a patient's question.

Optimizing LLMs for patient-driven queries, and patient education and counseling around the use of LLMs, have potential to enhance patient use and understanding of their health information.

Acute myocardial infarction (AMI), commonly known as a heart attack, is one of the leading causes of death globally. While healthcare professionals possess fundamental knowledge of managing AMI, there are key areas that require improvement or where basic knowledge is lacking. Timely decision-making, collaboration with the healthcare team, and continuous patient monitoring are imperative for optimizing outcomes in AMI cases. This study aimed to determine the current knowledge level and educational needs of healthcare professionals at Jordan University Hospital regarding AMI management to develop a targeted training program.

To understand the current knowledge and educational needs of these healthcare professionals in managing AMI, a quantitative analysis was conducted using a sample of 309 internship doctors and registered nurses at Jordan University Hospital. Data were collected through questionnaire surveys, exploratory factor analysis, and hypothesis testing.

The data analysis revealed that a significant majority of the internship doctors and registered nurses (over 90%) have an excellent understanding and adequate knowledge concerning the management of AMI. However, there are gaps in certain areas of AMI management. Additionally, a significant relationship was found between the occupational category (registered nurses and internship doctors) and the management of AMI.

This study highlights the importance of focused educational interventions in improving healthcare workers' skills in managing AMI. By addressing knowledge gaps through customized training content tailored to different professional roles, Jordan University Hospital can enhance the standard of care provided to AMI patients.

Periprosthetic joint infection (PJI) represents one of the most devastating complications following total joint arthroplasty, often necessitating additional surgeries and antimicrobial therapy, and potentially leading to disability. This significantly increases the burden on both patients and the healthcare system. Given the considerable suffering caused by PJI, its prevention and treatment have long been focal points of concern. However, challenges remain in accurately assessing individual risk, preventing the infection, improving diagnostic methods, and enhancing treatment outcomes. The development and application of artificial intelligence (AI) technologies have introduced new, more efficient possibilities for the management of many diseases. In this article, we review the applications of AI in the prevention, diagnosis, and treatment of PJI, and explore how AI methodologies might achieve individualized risk prediction, improve diagnostic algorithms through biomarkers and pathology, and enhance the efficacy of antimicrobial and surgical treatments. We hope that through multimodal AI applications, intelligent management of PJI can be realized in the future.

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.

Multimorbidity amplifies healthcare burdens due to the intricate requirements of patients and the pathophysiological complexities of multiple diseases. To address this, digital health technologies play a crucial role in effective healthcare delivery, requiring comprehensive evidence on their applications in managing multimorbidity. Therefore, this scoping review aims to identify various types of digital health technologies, explore their mechanisms, and identify barriers and facilitators within the context of multimorbidity.

This scoping review follows the Preferred Reporting Items for Scoping Reviews guidelines. PubMed, Scopus, Web of Science, EMBASE, and Google Scholar were used to search articles. Data extraction focused on study characteristics, types of health technologies, mechanisms, outcomes, challenges, and facilitators. Results were presented using figures, tables, and texts. Thematic analysis was employed to describe mechanisms, impacts, challenges, and strategies related to digital health technologies in managing multimorbidity.

Digital health technology encompasses smartphone apps, wearable devices, and platforms for remote healthcare (telehealth). These technologies work through care coordination, collaboration, communication, self-management, remote monitoring, health data management, and tele-referrals. Digital health technologies improved quality of care and life, cost efficiency, acceptability of care, collaboration, streamlined healthcare delivery, reduced workload, and bridging knowledge gaps. Patients' and healthcare providers' resistance and skills, lack of support (technical, financial, and infrastructure), and ethical concerns (e.g., privacy) barred digital health technologies implementation. Arranging organization, providing technical support, employing care coordination strategies, enhancing acceptability, deploying appropriate technology, considering patient needs, and adhering with ethical principles facilitate digital health technologies implementation.

Digital health technology holds significant promise in improving care for individuals with multimorbidity by enhancing coordination, self-management, and monitoring. Successful implementation requires addressing challenges such as patient resistance and infrastructure limitations through targeted strategies and investments. It is also essential to consider usability, privacy, and trustworthiness when adopting these tools.

Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care.

While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science.

We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality.

We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand.

We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Inflammatory bowel disease is marked by significant clinical heterogeneity, posing challenges for accurate diagnosis and personalized treatment strategies. Conventional approaches, such as endoscopy and histology, often fail to adequately and accurately predict medium- and long-term outcomes, leading to suboptimal patient management. Artificial intelligence is emerging as a transformative force enabling standardized, accurate, and timely disease assessment and outcome prediction, including therapeutic response. Artificial intelligence-driven intestinal barrier healing assessment provides novel insights into deep healing, facilitating the discovery of novel therapeutic targets. In addition, the automated integration of multi-omics data can enhance patient profiling and personalized management strategies. The future of inflammatory bowel disease care lies in the artificial intelligence-enabled "endo-histo-omics" integrative real-time approach, harmoniously fusing endoscopic, histologic, and molecular data. Despite challenges in its adoption, this paradigm shift has the potential to refine risk stratification, improve therapeutic precision, and enable personalized interventions, ultimately advancing the implementation of precision medicine in routine clinical practice.

Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry settings. Numerous notable healthcare institutions, such as Johns Hopkins University, Cleveland Clinic, and Mayo Clinic, have demonstrated measurable advancements in the use of artificial intelligence in healthcare delivery. Community pharmacies have seen a 40% increase in drug adherence and a 55% reduction in missed prescription refills since implementing artificial intelligence (AI) technologies. According to reports, hospital implementations have reduced prescription distribution errors by up to 75% and enhanced the detection of adverse medication reactions by up to 65%. Numerous businesses, such as Atomwise and Insilico Medicine, assert that they have made noteworthy progress in the creation of AI-based medical therapies. Emerging technologies like federated learning and quantum computing have the potential to boost the prediction of protein-drug interactions by up to 300%, despite challenges including high implementation costs and regulatory compliance. The significance of upholding patient-centred care while encouraging technology innovation is emphasised in this review.

The COVID-19 pandemic accelerated the development of AI-driven tools to improve public health surveillance and outbreak management. While AI programs have shown promise in disease surveillance, they also present issues such as data privacy, prejudice, and human-AI interactions. This sixth session of the of the WHO Pandemic and Epidemic Intelligence Innovation Forum examines the use of Artificial Intelligence (AI) in public health by collecting the experience of key global health organizations, such the Boston Children's Hospital, the Global South AI for Pandemic & Epidemic Preparedness & Response (AI4PEP) network, Medicines Sans Frontières (MSF), and the University of Sydney. AI's utility in clinical care, particularly in diagnostics, medication discovery, and data processing, has resulted in improvements that may also benefit public health surveillance. However, the use of AI in global health necessitates careful consideration of ethical issues, particularly those involving data use and algorithmic bias. As AI advances, particularly with large language models, public health officials must develop governance frameworks that stress openness, accountability, and fairness. These systems should address worldwide differences in data access and ensure that AI technologies are tailored to specific local needs. Ultimately, AI's ability to improve healthcare efficiency and equity is dependent on multidisciplinary collaboration, community involvement, and inclusive AI designs in ensuring equitable healthcare outcomes to fit the unique demands of global communities.

Ossification of the posterior longitudinal ligament (OPLL) is a significant contributor for unintentional durotomy following anterior spinal surgery, neural compression, and cervical myelopathy. While traditional diagnostic methods like plain radiography are commonly used, they may yield false negatives. The diagnostic accuracy and reliability of artificial intelligence methods for detecting this condition remain largely unexplored. This study aimed to systematically evaluate the performance of deep learning models (DLMs) in diagnosing and predicting cervical OPLL.

This systematic review assesses the utilization of DLMs in diagnosing and predicting OPLL. Inclusion criteria were defined as the use of DLM for the diagnosis and prediction of cervical OPLL in adult patients. Databases included PubMed, Google Scholar, Cochrane Library, ScienceDirect, and BASE. The risk of bias was assessed using the QUADAS-2 tool.

Seven studies with a pooled sample size of 3,373 patients were included. The pooled accuracy, area under the curve, sensitivity, and accuracy are 0.93, 0.92, 0.88, and 0.9. DLM demonstrated superior diagnostic performance, outperforming human comparator groups in terms of sensitivity (0.86 vs. 0.77), specificity (0.98 vs. 0.74), and accuracy (0.89 vs. 0.76). The meta-analysis with a pooled sample size of 1,016 patients revealed the highest proportion of right-identified OPLL subtypes in the mixed- and continuous subtypes (0.93 and 0.87). Accuracy and sensitivity of DLM were higher in the upper compared to the lower cervical spine.

Despite limitations in methodological variations and deep learning challenges, the findings support integrating these models into diagnostic protocols. Their robust performance suggests potential value in clinical practice, offering improved diagnostic accuracy and enhanced subtype differentiation.

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.

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.

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.

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.

The rising prevalence of obesity presents significant health, economic, and social challenges, necessitating a comprehensive approach to prevention, diagnosis, treatment, and long-term management. This review highlights the transformative role of artificial intelligence in obesity medicine, showcasing how technologies such as machine learning, deep learning, natural language processing, and large language models improve obesity management. The capacity of artificial intelligence to analyze extensive datasets enables predictive analytics, personalized treatment plans, and real-time behavioral interventions. Despite its potential, integrating artificial intelligence in obesity medicine faces challenges and ethical considerations, such as data privacy, algorithmic bias, artificial intelligence hallucination, transparency, and implementation barriers.

The landscape of healthcare is rapidly changing with the increasing usage of machine and deep learning artificial intelligence and digital tools to assist in various sectors. This study aims to analyze the feasibility of the implementation of artificial intelligence (AI) models into healthcare systems. This review included English-language publications from databases such as SCOPUS, PubMed, and Google Scholar between 2000 and 2024. AI integration in healthcare systems will assist in large-scale dataset analysis, access to healthcare information, surgery data and simulation, and clinical decision-making in addition to many other healthcare services. However, with the reliance on AI, issues regarding medical liability, cybersecurity, and health disparities can form. This necessitates updates and transparency on health policy, AI training, and cybersecurity measures. To support the implementation of AI in healthcare, transparency regarding AI algorithm training and analytical approaches is key to allowing physicians to trust and make informed decisions about the applicability of AI results. Transparency will also allow healthcare systems to adapt appropriately, provide AI services, and create viable security measures. Furthermore, the increased diversity of data used in AI algorithm training will allow for greater generalizability of AI solutions in patient care. With the growth of AI usage and interaction with patient data, security measures and safeguards, such as system monitoring and cybersecurity training, should take precedence. Stricter digital policy and data protection guidelines will add additional layers of security for patient data. This collaboration will further bolster security measures amongst different regions and healthcare systems in addition to providing more means to innovative care. With the growing digitization of healthcare, advancing cybersecurity will allow effective and safe implementation of AI and other digital systems into healthcare and can improve the safety of patients and their personal health information.

The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision) advance evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes(formulating clinical questions, conducting literature searches, appraising evidence, and developing clinical guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, and enabling real-time updates to guidelines. AI supports personalized medicine by analyzing large datasets, genetic information and electronic health records (EHRs), to tailor diagnostic and treatment plans to patient profiles. Predictive analytics enhance outcomes by leveraging historical data and ongoing monitoring to predict responses and optimize care pathways. Despite the transformative potential, there are challenges. The accuracy, transparency, and explainability of AI algorithms is critical for gaining trust and ensuring ethical deployment. Integration into existing clinical workflows requires collaboration between AI developers and users to ensure seamless user-friendly adoption. Ethical considerations, such as privacy,data security, and algorithmic bias, must also be addressed to mitigate risks and ensure equitable healthcare delivery. Regulatory frameworks, eg. The EU AI Regulation, emphasize transparency, data governance, and human oversight, particularly for high-risk AI systems. The economic and operational benefits are cost savings, improved diagnostic precision, and enhanced patient outcomes. Future trends (federated learning and self-supervised learning), will enhance the scalability and applicability of AI in EBLM, paving the way for a new era of precision medicine. AI in EBLM has the potential to transform healthcare delivery, improve patient outcomes, and advance personalized/precision medicine.

Background/Objectives: Artificial intelligence (AI) is transforming healthcare, enabling advances in diagnostics, treatment optimization, and patient care. Yet, its integration raises ethical, regulatory, and societal challenges. Key concerns include data privacy risks, algorithmic bias, and regulatory gaps that struggle to keep pace with AI advancements. This study aims to synthesize a multidisciplinary framework for trustworthy AI in healthcare, focusing on transparency, accountability, fairness, sustainability, and global collaboration. It moves beyond high-level ethical discussions to provide actionable strategies for implementing trustworthy AI in clinical contexts. Methods: A structured literature review was conducted using PubMed, Scopus, and Web of Science. Studies were selected based on relevance to AI ethics, governance, and policy in healthcare, prioritizing peer-reviewed articles, policy analyses, case studies, and ethical guidelines from authoritative sources published within the last decade. The conceptual approach integrates perspectives from clinicians, ethicists, policymakers, and technologists, offering a holistic "ecosystem" view of AI. No clinical trials or patient-level interventions were conducted. Results: The analysis identifies key gaps in current AI governance and introduces the Regulatory Genome-an adaptive AI oversight framework aligned with global policy trends and Sustainable Development Goals. It introduces quantifiable trustworthiness metrics, a comparative analysis of AI categories for clinical applications, and bias mitigation strategies. Additionally, it presents interdisciplinary policy recommendations for aligning AI deployment with ethical, regulatory, and environmental sustainability goals. This study emphasizes measurable standards, multi-stakeholder engagement strategies, and global partnerships to ensure that future AI innovations meet ethical and practical healthcare needs. Conclusions: Trustworthy AI in healthcare requires more than technical advancements-it demands robust ethical safeguards, proactive regulation, and continuous collaboration. By adopting the recommended roadmap, stakeholders can foster responsible innovation, improve patient outcomes, and maintain public trust in AI-driven healthcare.

A discharge summary is an important clinical document that summarizes a patient's clinical information and relevant events that occurred during hospitalization. It serves as a detailed handover of the patient's most recent and updated medical case records to general practitioners, who continue longitudinal follow-up with patients in the community and future medical care providers. A copy of the redacted/abbreviated form of the discharge summary is also usually given to patients and their caregivers so that important information, such as diagnoses, medication changes, return advice, and follow-up plans, is clearly documented. However, in reality, as discharge summaries are often written by junior physicians who may be inexperienced or have lacked medical training in this area, clinical audits often reveal poorly written discharge summaries that are unclear, inaccurate, or lack important details. Therefore, in this article, we sought to develop a simple "DISCHARGED" framework that outlines the important components of the discharge summary that we derived from a systematic search of relevant literature and further discuss several pedagogical strategies for training and assessing discharge summary writing.

Artificial intelligence (AI) is not merely a tool for the future of clinical medicine; it is already reshaping the landscape, challenging traditional paradigms, and expanding the horizons of what is achievable in healthcare [...].

Type 2 diabetes (T2D) is the fastest-growing non-communicable disease worldwide, accounting for around 90% of all diabetes cases and imposing a significant health burden globally. Due to its phenotypic heterogeneity and composite genetic underpinnings, T2D requires a precision medicine approach personalized to individual molecular profiles, thereby shifting away from the traditional "one-size-fits-all" medical methods. This review advocates for a thorough pharmacomultiomics approach to enhance precision medicine for T2D. It emphasizes personalized treatment strategies that enhance treatment efficacy while minimizing adverse effects by integrating data from genomics, proteomics, metabolomics, transcriptomics, microbiomics, and epigenomics. We summarize key findings on candidate genes impacting diabetic medication responses and explore the potential of pharmacometabolomics in predicting drug efficacy. The role of pharmacoproteomics in prognosis and discovering new therapeutic targets is discussed, along with transcriptomics' contribution to understanding T2D pathophysiology. Additionally, pharmacomicrobiomics is explored to understand gut microbiota interactions with antidiabetic drugs. Emerging evidence on utilizing epigenomic profiles in improving drug efficacy and personalized treatment is also reviewed, illustrating their implications in personalized medicine. In this paper, we discuss the integration of these layers of omics data, examining recently developed paradigms that leverage complex data to deepen our understanding of diabetes. Such integrative approaches advance precision medicine strategies to tackle the disease by better understanding its complex biology.

The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions.

Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, and immunology data. This comprehensive review explores the clinical applications of AI-driven analytics in unlocking personalized insights for patients with autoimmune rheumatic diseases. Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. Deep learning techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, and MHC-peptide binding predictions in genetics. AI-enabled immunology data analysis, including dimensionality reduction, cell population identification, and sample classification, provides unprecedented insights into complex immune responses. The review highlights real-world examples of AI-driven precision medicine platforms and clinical decision support tools in rheumatology. Evaluation of outcomes demonstrates the clinical benefits and impact of these approaches in revolutionizing patient care. However, challenges such as data quality, privacy, and clinician trust must be navigated for successful implementation. The future of precision medicine lies in the continued research, development, and clinical integration of AI-driven strategies to unlock personalized patient care and drive innovation in rheumatology.