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.

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.

Objective: To examine how algorithmic fairness is measured, operationalized, and reported in machine learning (ML) models designed to predict or support secondary prevention of cardiovascular disease (CVD) outcomes including progression, recurrence, readmission, and post-index mortality in racialized populations. Materials and Methods: This scoping review was conducted in accordance with PRISMA-ScR guidelines and registered with the Open Science Framework (OSF registration: https://doi.org/10.17605/OSF.IO/9W67V). Systematic searches of OVID MEDLINE, EMBASE, Scopus, and CENTRAL were performed to identify studies evaluating fairness in ML models applied to secondary cardiovascular outcomes. Results: Of 2669 records screened, three retrospective cohort studies met inclusion criteria. All studies used large-scale electronic health record data from the United States and evaluated model performance across racial subgroups. Only one study implemented fairness-aware model development, reporting improvements of approximately 5%-12% in equity-related metrics, accompanied by modest trade-offs in calibration and sensitivity. The remaining studies assessed fairness post hoc and demonstrated limited ability to mitigate subgroup performance differences. Discussion: Most full-text studies excluded during screening addressed fairness in predicting primary CVD incidence rather than secondary outcomes, highlighting a substantial gap in the literature. Across included studies, observed fairness limitations appeared to be driven largely by upstream structural and data-generating factors such as representation, care patterns, and documentation rather than algorithmic design alone. Conclusion: Evidence on algorithmic fairness in ML models for secondary cardiovascular outcomes remains sparse. Improved reporting of subgroup performance, missingness, and calibration, alongside integration of fairness throughout model development, is necessary before equitable clinical deployment. Study Registration: Open Science Framework: https://doi.org/10.17605/OSF.IO/9W67V