Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.

ABSTRACT:

Background

Large language models (LLMs), such as GPT-3.5 and GPT-4 (OpenAI), have been transforming virtual patient systems in medical education by providing scalable and cost-effective alternatives to standardized patients. However, systematic evaluations of their performance, particularly for multimorbidity scenarios involving multiple coexisting diseases, are still limited.

Objective

This systematic review aimed to evaluate LLM-based virtual patient systems for medical history-taking, addressing four research questions: (1) simulated patient types and disease scope, (2) performance-enhancing techniques, (3) experimental designs and evaluation metrics, and (4) dataset characteristics and availability.

Methods

Following PRISMA (Preferred Reporting Items for Systemati

SUBMITTER: Li D 

PROVIDER: S-EPMC12811743 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature