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arXiv · 2609.22517

Scalable AI-based clinical communication training and automated assessment

Masum Hasan·Ron Epstein·Thomas Carroll·Ehsan Hoque
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Abstract

Poor clinical communication can delay care, contribute to errors, and harm patients, yet opportunities for repeated practice with feedback remain limited. Our prior randomized trial showed that practice with the SOPHIE AI patient platform improved serious illness communication, but the system addressed a single clinical context and required human effort for delivery and assessment. We developed SOPHIE 2.0, a browser-based, self-service platform integrating embodied AI-patient interactions, personalized feedback, and automated assessment across 24 clinical scenarios. An automated large language model assessor evaluated three communication skills---Empower, Be Explicit, and Empathize---with agreement comparable to individual human raters ($r=0.759$; ICC$=0.746$). In a study of 59 clinicians and students, participants completed two AI-patient encounters with personalized feedback; 92% found the platform engaging, 86% easy to use, and 83% clinically relevant. Scores were higher in the second encounter, though the uncontrolled design precludes attributing this change specifically to training.

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Masum Hasan, Ron Epstein, Thomas Carroll, Ehsan Hoque. 2026-09-18. Scalable AI-based clinical communication training and automated assessment. https://arxiv.org/abs/2609.22517

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