Use of artificial intelligence–enhanced virtual patients in educational approaches to medical interview training: a systematic review

Abstract

Effective communication and empathy are essential for professional–patient relationships, patient care, and the therapeutic alliance. With the increasing use of artificial intelligence-assisted virtual patients (AI-VPs), new opportunities to improve interview training are observed. This systematic review assesses the educational use, benefits, and limitations of AI-VPs in clinical interview training for healthcare professionals and students. Following PRISMA recommendations, the review involved a systematic search in PubMed, Google Scholar, and PsycINFO. Eleven studies met the inclusion criteria. Methodological quality of papers was assessed using the MMAT 2018. The findings suggest that AI-VPs can improve empathy, communication, and clinical reasoning, as well as promote perceived self-efficacy, reduce anxiety, and enhance engagement through immersive experiences. However, questions remain about the transferability of these skills to clinical practice. The studies show a lack of consistent theoretical frameworks and standardized outcome measures. While feedback is widely used, it is not formalized. Technical limitations, such as issues with speech recognition and nonverbal communication, can disrupt immersion. AI-VPs represent a promising educational tool for initial interview training, as it creates learning environments that are scalable, standardized, and interactive. A step-by-step approach, beginning with standardized patients and progressing to real patients, may optimize skill acquisition. Robust longitudinal studies and cost–benefit analyses are needed to support these findings. Although AI-VPs can improve training in interpersonal and communication skills, further work is needed to validate its clinical impact and educational design.

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