A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training

• URL: http://arxiv.org/abs/2606.28526v1
• Date: Fri, 26 Jun 2026 18:21:58 GMT
• Title: A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
• Abstract summary: We introduce a French OSCE dialogue dataset comprising 240 student-patient training interactions.<n>We build upon it a controllable LLM-based pipeline to generate synthetic OSCE dialogues.<n>We also propose a multi-level evaluation framework assessing patient simulation quality, student performance, and linguistic quality.
• Score: 4.13196819783484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The clinical and communication skills of medical students are commonly assessed through Objective Structured Clinical Examinations (OSCEs), which consist of brief scenario-driven simulations of doctor-patient interactions. However, training is often limited by the low availability of human standardized patients, motivating the development of realistic virtual patients (VPs). To address this gap, we introduce a French OSCE dialogue dataset comprising 240 student-patient training interactions. We build upon it a controllable LLM-based pipeline to generate synthetic OSCE dialogues. The pipeline integrates modular components, such as retrieval-based grounding and a reflection loop, to ensure patient fidelity, coherence, and realism. Additionally, we propose a multi-level evaluation framework assessing patient simulation quality, student performance, and linguistic quality, using an LLM-as-a-Judge approach. Experiments suggest that controllability modules generally improve patient fidelity and student evaluation consistency. Finally, we implement an interactive prototype in which students can practice with a VP and receive automatic feedback.

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