PurposeTo systematically identify and synthesize peer-reviewed literature describing implemented AI innovations within undergraduate medical education clinical skills curricula from January 2022 through January 2026.MethodThe authors conducted a scoping review querying PubMed and Scopus, supplemented by SciSpace as an AI-assisted citation discovery tool. Eligible studies described utilizing AI to deliver the clinical skills curriculum in innovative ways (e.g., instruction in history-taking, communication, clinical reasoning, clinical documentation, OSCE/simulation assessment). We extracted data into standardized templates and thematically sorted to characterize how AI-assisted instruction was being implemented across educational objectives.ResultsFrom 1,130 initial records, 39 studies met inclusion criteria. AI-assisted instruction clustered into eight thematic categories: LLM-Based Virtual Patient and Clinical Simulation Systems (n = 19), AI-Augmented OSCE and Simulation Assessment Tools (n = 6), Embodied and Robotic AI Clinical Simulations (n = 4), AI-Supported Procedural and Technical Skills Training (n = 3), AI-Assisted Clinical Documentation and EHR-Based Skills Training (n = 2), Multimodal Analytics for Skills Assessment (n = 2), Educator-Facing AI Case Authoring and Simulation Design Tools (n = 2), and AI-Supported Clinical Reasoning and Tutoring Tools (n = 1). Publication activity concentrated heavily in 2024–2025, with virtual patient applications representing the dominant category.ConclusionsAI implementation in clinical skills education has accelerated substantially since 2022, with large language model-powered virtual patient simulations emerging as the predominant application. Current implementations primarily position AI as a supplementary formative tool rather than a replacement for established pedagogical approaches. Robust evidence regarding long-term educational impact remains limited, indicating need for rigorous longitudinal evaluation alongside continued innovation.
Performance of large language models in delivering accurate and comprehensible patient information on heart failure and cardiomyopathy
BackgroundLarge language models (LLMs) are increasingly used by patients seeking cardiovascular health information through digital platforms. However, their accuracy and suitability for providing guidance on