Electronic Medical Records (EMRs) are central to the modern healthcare system. Recent advances in artificial intelligence (AI), particularly generative artificial intelligence (GenAI), have opened new opportunities for the advancement of EMRs. This scoping review aims to explore the current real-world applications of GenAI within EMRs to support an understanding of AI applications in healthcare. A literature search was conducted following PRISMA-ScR guidelines. The search was conducted using Ovid MEDLINE, up to 28 October 2024, using a peer-reviewed search strategy. Overall, 55 studies were included. A list of five themes was generated by human reviewers based on the literature review: data manipulation (24), patient communication (9), clinical decision making (8), clinical prediction (8), summarization (4), and other (2). The majority of studies originated from the United States (35). Both proprietary and commercially available models were tested, with ChatGPT being the most commonly referenced LLM. As these models continue to be developed, their diverse use cases within EMRs have the potential to improve patient outcomes, enhance access to medical data, streamline hospital workflows, and reduce physician workload. However, continued problems surrounding data privacy, trust, bias, model hallucinations, and the need for robust evaluation remain. Further research considering the ethical, medical, and societal implications of GenAI applications in EMRs is essential to validate these findings and address existing limitations to support healthcare advancement.

Large language model-based virtual patients (LLM-VPs) are an emerging simulation tool for health professions education, but their design and integration into curricula are not well characterized. This scoping review mapped how LLM-VPs are being used for simulated clinical learning across health professions. Following a protocol registered on OSF, we searched MEDLINE, EMBASE, CENTRAL, Scopus, and Web of Science to 11 April 2025, per PRISMA-ScR guidelines, and included 21 studies that used LLMs to generate virtual patients for simulated clinical encounters. Data were extracted on technical design, fidelity domains, curricular integration, human factors, and Technology Acceptance Model constructs, and synthesized narratively. Most studies (n = 11) were pilot or feasibility evaluations with small samples (median 21) and used GPT-based models with dynamic text chat. Integration was limited to 10 studies that operated as pilots, 7 as electives, and 3 as core curricular components. The outcomes focused on Level 2 learning (clinical reasoning and preclinical OSCE performance), with predominantly self-report assessments. No studies reported Level 3 or 4 outcomes. Fidelity was strongest in cognitive, socio-cultural, and emotional domains, and 11 studies reported hallucinations or inaccurate outputs. LLM-VPs appear feasible and well-received but remain early-stage, underscoring the need for fidelity-aligned design and more rigorous, longitudinal evaluations.

Background: Generative artificial intelligence (AI) tools including ChatGPT have the potential to augment the process of designing examinations and assessments for medical learners, leading to time and resource savings, and the ability to produce large volumes of practice problems tailored to learner-specific strengths and weaknesses. Methods: This study compares the quality of free-text assessment problems and answer keys generated by ChatGPT to those produced by faculty educators for a renal and hematology curriculum subunit. Five expert reviewers reviewed a collection of 21 free-text assessment problems, 9 from a collection of historical assessment problems used in an undergraduate medical program and 12 produced with ChatGPT. Reviewers assigned a score from 1 to 5, reflecting the overall quality. Results: The average quality of problems generated by ChatGPT was greater than that of human-generated problems (4.00 vs. 2.71, p < 0.001). Using ordinal mixed-effect modeling, human-generated problems had significantly lower odds of receiving higher ratings than ChatGPT-generated problems (β = −2.43, 95% confidence interval −3.34 to −1.51, p < 0.001). Conclusions: It is suggested that ChatGPT can assist expert faculty educators in producing assessment tools, with direct benefits to medical learners, although it cannot entirely replace this role in its current state.

Ondansetron frequently triggers medication safety alerts because of its listing as a ‘known risk’ QT interval‐prolonging medication. We aimed to summarize literature on QT‐prolongation‐related major adverse cardiac events associated with ondansetron. We searched Medline, Embase, International Pharmaceutical Abstracts, and Cochrane Central for randomized controlled trials comparing ondansetron to placebo in adults. Major adverse cardiac events (MACE) included death, nonfatal cardiac arrest, ventricular tachyarrhythmia including torsades de pointes, seizure, or syncope. Random‐effects meta‐analyses were performed with a treatment arm continuity correction for single‐ and double‐zero event studies. We included 170 randomized trials ( n = 23,421, 70.7% female, 48.3% aged >65), 119 trials (70.0%) involving surgical patients, and 77 (45.4%) including electrocardiograms. Mean follow‐up was 10 days (SD: 21.8) (median: 1 day). Risk of bias was high for 11.8% of trials, and low for 28.8%. Only seven MACE (all deaths) were reported. Ondansetron was not associated with increased mortality ( n = 23,421, RR: 1.03, 95% CI: 0.76–1.39, I ² = 0.0%), or arrhythmias. Lack of events precluded prespecified meta‐analyses. Further research on QT‐prolonging medications and their attributed adverse cardiac events and medication alert optimization is needed.

Abdominal aortic aneurysm is a critical medical condition typically diagnosed using imaging such as CT of the abdomen and/or ultrasonography. Physical examination is often considered unreliable for definitive diagnosis. This case report presents a unique case of a male patient in their 70s with a large, visible abdominal aortic aneurysm on inspection of the abdomen, seen as a pulsating mass. This case report highlights the relevance of physical examination to support the diagnosis and management of abdominal aortic aneurysms.

Fever in the returning traveller is a challenging presentation that requires a wide array of clinical expertise and draws on complex medical decision-making. Recent advancements in artificial intelligence have shown it can be a helpful clinical support tool in this context. This scoping review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and registered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/BSPKX ), aimed to identify the current state of artificial intelligence use in assessing fever in the returning traveller. Here we describe the three eligible studies evaluating three distinct AI approaches identified in the literature. These heterogenous studies reported promising performance on study-specific endpoints in controlled settings. Given the lack of external validation, narrow populations studied, and absence of real-world implementation or user acceptance testing, further research is needed to explore this technology in a real-world clinical setting. The limited available evidence suggests that AI may have future potential to support assessment of fever in the returning traveller; however, clinical utility, safety, and real-world effectiveness remain unestablished. Large language models represent an emerging area of interest but were not evaluated in the studies included in this review.