# LLM-Powered Virtual Patient Agents for Interactive Clinical Skills Training with Automated Feedback

arXiv: https://arxiv.org/abs/2508.13943 · HTML: https://mlsanity.com/paper/2508.13943/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2025.

## Built on by (most cited first, 2 of 2)

- [A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training](https://mlsanity.com/paper/2606.28526/) (2026)
- [CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models](https://mlsanity.com/paper/2608.26147/) (2026)

## Abstract (arXiv)

Objective Structured Clinical Examinations (OSCEs) are essential for medical training, but they require significant resources, including professional actors and expert medical feedback. Although Large Language Models (LLMs) have introduced text-based virtual patients for communication practice, these simulations often lack the capability for richer, non-textual interactions. This paper presents a novel framework that significantly enhances LLM-based simulated patients by equipping them with action spaces, thereby enabling more realistic and dynamic patient behaviors that extend beyond text. Furthermore, our system incorporates virtual tutors that provide students with instant, personalized feedback on their performance at any time during these simulated encounters. We have conducted a rigorous evaluation of the framework's real-time performance, including system latency and component accuracy. Preliminary evaluations with medical experts assessed the naturalness and coherence of the simulated patients, as well as the usefulness and appropriateness of the virtual tutor's assessments. This innovative system provides medical students with a low-cost, accessible platform for personalized OSCE preparation at home.