A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
arXiv:2606.28526v1 Announce Type: new 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 g
The increasing sophistication of LLMs and the recognition of limitations in traditional medical training methods create an opportune moment for virtual patient systems.
This development represents a practical application of AI in high-stakes training, potentially improving medical education quality and accessibility.
The reliance on human standardized patients may decrease, and medical training can become more scalable and consistent through AI-powered simulations.
- · Medical Education Institutions
- · AI/LLM Developers
- · Healthcare Systems
- · Human Standardized Patient Programs (niche)
- · Traditional Simulation Manufacturers
Medical students gain more flexible and consistent training opportunities for clinical and communication skills.
The standardization of medical training across different institutions could improve through widespread adoption of such systems.
Improved physician communication skills from advanced training could lead to better patient outcomes and satisfaction.
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