ArticleCommunications medicine2025
Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GenAI-Supported Virtual Patients in Health Care Education: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Prostate cancer research on social media platforms: a bibliometric and thematic analysis.Frontiers in oncology · 2026Pooled it
- Trial
- Real-Time Artificial Intelligence Diagnostic Copilot in Simulated Primary Care Consultations: Randomized Simulation Study.JMIR formative research · 2026Trial
- A Patient Simulation Framework for Risk Assessment of Conversational Health Care AI: Development and Evaluation Study.JMIR AI · 2026Article
- What Platform Scores Miss: Multidimensional Evaluation of AI Teaching Agents in Medical Education.JMIR medical education · 2026Article
- Multiagent Large Language Model Framework for Psychotherapy Fidelity Assessment in Motivational Interviewing and Cognitive Behavioral Therapy Training: Cross-Sectional, Simulation-Based Evaluation Study.JMIR medical education · 2026Article
- AI for Clinical Competency Assessment: Scoping Review of Methods and Applications.JMIR medical education · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- [Development and psychometric validation of the User Experience Scale for Artificial Intelligence QuestionZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- A Large Language Model-Powered Multiagent Framework Emulating Standardized Patients in Clinical Communication Skills Training: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Article
- Digital Narrative Medicine: Translating Stories Into Medical Education and Better Patient Care.Journal of medical Internet research · 2026Article
- A benchmark for evaluating diagnostic questioning efficiency of LLMs in patient conversations.Scientific reports · 2026Article
- Intention to Use Large Language Models Among Clinical Nurses in China With Prior Familiarity With or Experience Using LLMs: A Qualitative Study.Journal of nursing management · 2026Article
- Multimodal generative AI for pre-practicum mental health support competency development: a human-supervised framework for education-childcare partnerships.Frontiers in psychiatry · 2026Review
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Teaching Personalized Doctor-Patient Communication with AI: PerTRAIN - a Prototype for Interpersonally Responsive Virtual Patients in Medical Education.Perspectives on medical education · 2026Article
- Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education.Communications medicine · 2025Article
- VI-OCR: "Visually Impaired" optical character recognition pipeline for text accessibility assessment.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
26 authors.
Funding
Abstract
backgroundSimulated patient systems are vital in medical education and research, providing safe, integrative training environments and supporting clinical decision-making. Progressive Artificial Intelligence (AI) technologies, such as Large Language Models (LLM), could advance simulated patient systems by replicating medical conditions and patient-doctor interactions with high fidelity and low cost. However, effectiveness and trustworthiness remain challenging.
methodsWe developed AIPatient, a simulated patient system powered by LLM-based AI agents. The system incorporates the Retrieval Augmented Generation (RAG) framework, powered by six task-specific LLM-based AI agents for complex reasoning. For simulation reality, the system is also powered by the AIPatient KG (Knowledge Graph), built with de-identified real patient data from the Medical Information Mart for Intensive Care (MIMIC)-III database.
resultsHere we show that the system's accuracy in Electronic Health Record (EHR)-based medical Question Answering (QA), readability, robustness, and stability. Specifically, the system achieves a QA accuracy of 94.15% when all six agents, surpassing benchmarks with partial or no agent integration. Its knowledgebase demonstrates high validity (F1 score=0.89). Readability scores show median Flesch Reading Ease at 68.77 and median Flesch Kincaid Grade at 6.4, indicating accessibility to all medical professionals. Robustness and stability are confirmed with non-significant variance (ANOVA F-value = 0.6126, p > 0.1; F-value = 0.782, p > 0.1). A user study with medical students shows that AIPatient delivers high fidelity, usability, and educational value, matching or exceeding human-simulated patients in history-taking.
conclusionsLarge language model-based simulated patient systems provide accurate, readable, and reliable medical encounters and demonstrates potential to transform medical education.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.