An AI-Driven Virtual Patient for Breaking Bad News: An Expert Formative Study on Facial Expression Intensity

Abstract

Interactive virtual patients driven by large language models (LLMs) offer scalable solutions for medical communication training, such as breaking bad news. However, designing their emotional expressiveness remains a challenge. This paper presents an AI-driven virtual patient framework combining LLM dialogue with real-time facial animation in virtual reality (VR). We conducted an exploratory, formative evaluation with seven medical experts to gather early feedback and elicit design requirements. The evaluation focused on how variations in facial expression intensity affect perceived realism and the virtual patient's emotion intelligibility. While descriptive quantitative ratings remained baseline across conditions, qualitative interviews provided deep insights into how experts perceive virtual emotional cues. The findings suggest that experts evaluate emotional realism holistically through multiple verbal and non-verbal channels; isolated facial adjustments are easily overshadowed by dialogue nuances and vocal prosody. Based on these insights, we present a roadmap for future medical training simulators, highlighting the need for synchronized, multi-modal pipelines incorporating body gestures, conversational pauses, and vocal dynamics.