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

arXiv: https://arxiv.org/abs/2606.28526 · HTML: https://mlsanity.com/paper/2606.28526/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [A Zero-Shot Language Agent for Computer Control with Structured Reflection](https://mlsanity.com/paper/2310.08740/) (2023)
- [Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data](https://mlsanity.com/paper/2408.13833/) (2024)
- [Synthetic Patient-Physician Dialogue Generation from Clinical Notes Using LLM](https://mlsanity.com/paper/2408.06285/) (2024)
- [DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain](https://mlsanity.com/paper/2402.13432/) (2024)
- [Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation](https://mlsanity.com/paper/2406.18460/) (2024)
- [Large Language Models for Medical OSCE Assessment: A Novel Approach to Transcript Analysis](https://mlsanity.com/paper/2410.12858/) (2024)
- [LLM-Powered Virtual Patient Agents for Interactive Clinical Skills Training with Automated Feedback](https://mlsanity.com/paper/2508.13943/) (2025)
- [Semantic Similarity To Improve Question Understanding in a Virtual Patient](https://mlsanity.com/paper/1912.07421/) (2019)
- [LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs](https://mlsanity.com/paper/2604.08126/) (2026)

## Abstract (arXiv)

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.