ArticleJMIR medical education2025
Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.
Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- Application of AI-based virtual standardized patients in physician-patient communication training: a study based on the SEGUE framework.Frontiers in public health · 2026Trial
- AI-Simulated Patients for Training Shared Decision-Making: Feasibility Study in Medical Education.JMIR medical education · 2026Article
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Enhancing medical communication skills through video-recorded peer role-play and a standardized checklist.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Effective communication is fundamental to high-quality health care delivery, influencing patient satisfaction, adherence to treatment plans, and clinical outcomes. However, communication skills training for medical undergraduates often faces challenges in scalability, resource allocation, and personalization. Traditional methods, such as role-playing with standardized patients, are resource intensive and may not provide consistent feedback tailored to individual learners' needs. Artificial intelligence (AI) offers realistic patient interactions for education. Objective: This study aims to investigate the application of AI communication training tools in medical undergraduate education within a primary care context. The study evaluates the effectiveness, usability, and impact of AI virtual patients (VPs) on medical students' experience in communication skills practice. Methods: The study used a mixed methods sequential explanatory design, comprising a quantitative survey followed by qualitative focus group discussions. Eighteen participants, including 15 medical students and 3 practicing doctors, engaged with an AI VP simulating a primary care consultation for prostate cancer risk assessment. The AI VP was designed using a large language model and natural voice synthesis to create realistic patient interactions. The survey assessed 5 domains: fidelity, immersion, intrinsic motivation, debriefing, and system usability. Focus groups were used to explore participants' experiences, challenges, and perceived educational value of the AI tool. Results: Significant positive responses emerged against a neutral baseline, with the following median scores: intrinsic motivation 16.5 of 20.0 (IQR 15.0-18.0; d=2.09, P<.001), system usability 12.0 of 15.0 (IQR 11.5-12.5; d=2.18, P<.001), and psychological safety 5.0 of 5.0 (IQR 5.0-5.0; d=4.78, P<.001). Fidelity (median score 6.0/10.0, IQR 5.2-7.0; d=-0.08, P=.02) and immersion (median score 8.5/15.0, IQR 7.0-9.8; d=0.25 P=.08) were moderately rated. The overall Immersive Technology Evaluation Measure scores showed a high positive learning experience: median 47.5 of 65.0 (IQR 43.0-51.2; d=2.00, P<.001). Qualitative analysis identified 3 major themes across 11 subthemes, with participants highlighting both technical limitations and educational value. Participants valued the safe practice environment and the ability to receive immediate feedback. Conclusions: AI VP technology shows promising potential for communication skills training despite the current realism limitations. While it does not yet match human standardized patient authenticity, the technology has achieved sufficient fidelity to support meaningful educational interactions, and this study identified clear areas for improvement. The integration of AI into medical curricula represents a promising avenue for innovation in medical education, with the potential to improve the quality and effectiveness of training programs.
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Registered trials
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