# Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education arXiv: https://arxiv.org/abs/2409.18924 ยท HTML: https://mlsanity.com/paper/2409.18924/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. - Builds on 9 of its 48 references that are in the map. - First posted on arXiv 27 Sep 2024. ## Placed in - LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/) ## Builds on (most cited first, 9 of 9) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles](https://mlsanity.com/paper/2407.00870/) (2024) - [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023) - [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023) - [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024) - [Towards Evaluating and Building Versatile Large Language Models for Medicine](https://mlsanity.com/paper/2408.12547/) (2024) - [Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities](https://mlsanity.com/paper/2406.02787/) (2024) - [Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis](https://mlsanity.com/paper/2403.16303/) (2024) - [CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference](https://mlsanity.com/paper/2308.03853/) (2023) ## Built on by (most cited first, 13 of 13) - [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025) - [Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications](https://mlsanity.com/paper/2508.00669/) (2025) - [MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors](https://mlsanity.com/paper/2604.06846/) (2026) - [Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies](https://mlsanity.com/paper/2505.22981/) (2025) - [Semantic Scheduling for LLM Inference](https://mlsanity.com/paper/2506.12204/) (2025) - [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026) - [ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning](https://mlsanity.com/paper/2512.23440/) (2025) - [Human-AI Co-reasoning for Clinical Diagnosis with Evidence-Integrated Language Agent](https://mlsanity.com/paper/2603.10492/) (2026) - [A Proactive Multi-Agent Dialogue Framework for Assessing Social Language Disorder Traits in Autism](https://mlsanity.com/paper/2605.22993/) (2026) - [AgentCAT: Simulating Computerized Adaptive Testing via Multi-Agent Large Language Models](https://mlsanity.com/paper/2606.21832/) (2026) - [Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking](https://mlsanity.com/paper/2606.26205/) (2026) - [EmoPatient: An Emotion-Directed Patient Simulator for Realistic Palliative Care Communication Training](https://mlsanity.com/paper/2608.07495/) (2026) - [Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training](https://mlsanity.com/paper/2609.10939/) (2026) ## Abstract (arXiv) Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Advances in artificial intelligence (AI), especially large language models (LLMs), can enhance simulated patients by replicating medical conditions and doctor patient interactions with high fidelity and at low cost, but effectiveness and trustworthiness remain open challenges. Methods: We developed AIPatient, a simulated patient system powered by LLM based AI agents. The system uses a retrieval augmented generation (RAG) framework with six task specific agents for complex reasoning. To improve realism, it is linked to the AIPatient knowledge graph built from de identified real patient data in the MIMIC III intensive care database. Results: We evaluated electronic health record (EHR) based medical question answering (QA), readability, robustness, stability, and user experience. AIPatient reached 94.15 percent QA accuracy when all six agents were enabled, outperforming versions with partial or no agent integration. The knowledge base achieved an F1 score of 0.89. Readability scores showed a median Flesch Reading Ease of 68.77 and a median Flesch Kincaid Grade of 6.4, indicating accessibility for most medical trainees and clinicians. Robustness and stability were supported by non significant variance in repeated trials (analysis of variance F value 0.61, p greater than 0.1; F value 0.78, p greater than 0.1). A user study with medical students showed that AIPatient provides high fidelity, usability, and educational value, comparable to or better than human simulated patients for history taking. Conclusions: LLM based simulated patient systems can deliver accurate, readable, and reliable medical encounters and show strong potential to transform medical education.