DIALOGUE: A Generative AI-Based Pre–Post Simulation Study to Enhance Diagnostic Communication in Medical Students Through Virtual Type 2 Diabetes Scenarios

Abstract

DIALOGUE (DIagnostic AI Learning through Objective Guided User Experience) is a generative artificial intelligence (GenAI)-based training program designed to enhance diagnostic communication skills in medical students. In this single-arm pre-post study, we evaluated whether DIALOGUE could improve students' ability to disclose a type 2 diabetes mellitus (T2DM) diagnosis with clarity, structure, and empathy. Thirty clinical-phase students completed two pre-test virtual encounters with an AI-simulated patient (ChatGPT, GPT-4o), scored by blinded raters using an eight-domain rubric. Participants then engaged in ten asynchronous GenAI scenarios with automated natural-language feedback. Seven days later, they completed two post-test consultations with human standardized patients, again evaluated with the same rubric. Mean total performance increased by 36.7 points (95% CI: 31.4-42.1; <i>p</i> < 0.001), and the proportion of high-performing students rose from 0% to 70%. Gains were significant across all domains, most notably in opening the encounter, closure, and diabetes specific explanation. Multiple regression showed that lower baseline empathy (β = -0.41, <i>p</i> = 0.005) and higher digital self-efficacy (β = 0.35, <i>p</i> = 0.016) independently predicted greater improvement; gender had only a marginal effect. Cluster analysis revealed three learner profiles, with the highest-gain group characterized by low empathy and high digital self-efficacy. Inter-rater reliability was excellent (ICC ≈ 0.90). These findings provide empirical evidence that GenAI-mediated training can meaningfully enhance diagnostic communication and may serve as a scalable, individualized adjunct to conventional medical education.

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