# Modeling Challenging Patient Interactions: LLMs for Medical Communication Training

arXiv: https://arxiv.org/abs/2503.22250 · HTML: https://mlsanity.com/paper/2503.22250/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024)
- [Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://mlsanity.com/paper/2408.15787/) (2024)
- [LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation](https://mlsanity.com/paper/2305.13614/) (2023)
- [Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language](https://mlsanity.com/paper/2311.14543/) (2023)
- [Leveraging Large Language Model as Simulated Patients for Clinical Education](https://mlsanity.com/paper/2404.13066/) (2024)
- [User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT](https://mlsanity.com/paper/2402.02136/) (2024)
- [Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://mlsanity.com/paper/2411.09539/) (2024)
- [From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant](https://mlsanity.com/paper/2409.15843/) (2024)
- [Med-Bot: An AI-Powered Assistant to Provide Accurate and Reliable Medical Information](https://mlsanity.com/paper/2411.09648/) (2024)

## Built on by (most cited first, 7 of 7)

- [RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems](https://mlsanity.com/paper/2509.10746/) (2025)
- [Human or LLM as Standardized Patients? A Comparative Study for Medical Education](https://mlsanity.com/paper/2511.14783/) (2025)
- [When Avatars Have Personality: Effects on Engagement and Communication in Immersive Medical Training](https://mlsanity.com/paper/2509.14132/) (2025)
- [An Agentic AI Framework for Training General Practitioner Student Skills](https://mlsanity.com/paper/2512.18440/) (2025)
- [Safeguarding Privacy: Privacy-Preserving Detection of Mind Wandering and Disengagement Using Federated Learning in Online Education](https://mlsanity.com/paper/2602.09904/) (2026)
- [Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering](https://mlsanity.com/paper/2604.12210/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

Effective patient communication is pivotal in healthcare, yet traditional medical training often lacks exposure to diverse, challenging interpersonal dynamics. To bridge this gap, this study proposes the use of Large Language Models (LLMs) to simulate authentic patient communication styles, specifically the "accuser" and "rationalizer" personas derived from the Satir model, while also ensuring multilingual applicability to accommodate diverse cultural contexts and enhance accessibility for medical professionals. Leveraging advanced prompt engineering, including behavioral prompts, author's notes, and stubbornness mechanisms, we developed virtual patients (VPs) that embody nuanced emotional and conversational traits. Medical professionals evaluated these VPs, rating their authenticity (accuser: 3.8 ± 1.0; rationalizer: 3.7 ± 0.8 on a 5-point Likert scale (from one to five)) and correctly identifying their styles. Emotion analysis revealed distinct profiles: the accuser exhibited pain, anger, and distress, while the rationalizer displayed contemplation and calmness, aligning with predefined, detailed patient description including medical history. Sentiment scores (on a scale from zero to nine) further validated these differences in the communication styles, with the accuser adopting negative (3.1 ± 0.6) and the rationalizer more neutral (4.0 ± 0.4) tone. These results underscore LLMs' capability to replicate complex communication styles, offering transformative potential for medical education. This approach equips trainees to navigate challenging clinical scenarios by providing realistic, adaptable patient interactions, enhancing empathy and diagnostic acumen. Our findings advocate for AI-driven tools as scalable, cost-effective solutions to cultivate nuanced communication skills, setting a foundation for future innovations in healthcare training.