JMIR Publications

ABSTRACT

Background:

Generative Artificial Intelligence (GenAI) is revolutionising virtual patient simulations in healthcare education, providing adaptable and interactive learning experiences. Traditional virtual patients rely on scripted responses, while GenAI enhances realism by generating dynamic, case-specific interactions.

Objective:

This systematic review aims to summarise and synthesise empirical research evaluating how GenAI-supported virtual patients influence student training and learning outcomes in healthcare education.

Methods:

A structured systematic review was conducted across CINAHL, Medline, Embase, Scopus, and Web of Science, identifying 391 studies, of which 10 met inclusion criteria after full-text screening. Articles were included if they were peer-reviewed, full-text, focused on GenAI-supported virtual patients in healthcare education, used quantitative or mixed methods, and were written in English. Studies focusing primarily on GenAI model development were excluded.

Results:

The review classified GenAI-supported virtual patients into three key design components: input modalities (text, voice, hybrid), output types (text, synthesised speech), and avatars (3D, non-embodied interfaces). Five studies employed experimental designs with control groups, but none implemented longitudinal assessments. Problem-based learning (PBL) was the only explicitly referenced educational theory, while most studies lacked pedagogical grounding. Self-reported user perceptions dominated evaluation metrics, limiting insights into actual learning outcomes and clinical competency development.

Conclusions:

Findings highlight the need for standardised study designs, extended interaction durations, and objective learning assessments. To maximise effectiveness, future research should integrate educational theories, refine GenAI interaction designs, and establish long-term evaluations. Insights and evidence-based recommendations are offered for educators, researchers, and policymakers seeking to enhance healthcare training through advanced technological interventions.