Large Language Model-Based Virtual Patient Simulations in Medical and Nursing Education: A Review

Abstract

Large language model (LLM)-based virtual patient (VP) simulations are emerging to complement traditional medical and nursing education by enabling safe, repeatable, and context-rich clinical practice. This review synthesizes recent developments from 2023 to 2025, mapping implementation approaches, data practices, evaluation methods, and cross-cutting challenges across forty studies. Six implementation categories are identified: scenario generation; prompt-driven VPs; feedback-integrated automated scoring; realism- and adaptability-enhanced systems; knowledge-driven and multi-agent hybrids; and mental health-oriented systems. The analysis summarizes dataset usage (including knowledge sources and governance) and evaluation frameworks, and it introduces quantitative indicators for reproducible assessment. Persistent challenges include factual accuracy, role consistency, emotional realism, and ethical and legal accountability. Overall, LLM-based VP systems show growing potential to extend simulation-based learning, but stronger evidence from multi-site controlled studies, standardized metrics, transparent reporting (model versions, prompts), and robust data governance is needed to establish educational validity and generalizability.

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