Large Language Model-Based Virtual Patients for Simulated Clinical Learning: A Scoping Review

Abstract

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.

Ähnliche Arbeiten