# MEDCO: Medical Education Copilots Based on A Multi-Agent Framework

arXiv: https://arxiv.org/abs/2408.12496 · HTML: https://mlsanity.com/paper/2408.12496/

## Key facts

- Cited by 33 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 22 of its 39 references that are in the map.
- First posted on arXiv 22 Aug 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, 22 of 22)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [Almanac: Retrieval-Augmented Language Models for Clinical Medicine](https://mlsanity.com/paper/2303.01229/) (2023)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://mlsanity.com/paper/2407.02483/) (2024)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation](https://mlsanity.com/paper/2304.12637/) (2023)
- [MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education](https://mlsanity.com/paper/2404.06711/) (2024)
- [ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs](https://mlsanity.com/paper/2305.15964/) (2023)
- [VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence](https://mlsanity.com/paper/2310.04992/) (2023)
- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)
- [OlympicArena Medal Ranks: Who Is the Most Intelligent AI So Far?](https://mlsanity.com/paper/2406.16772/) (2024)
- [VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis](https://mlsanity.com/paper/2403.10823/) (2024)

## Built on by (most cited first, 30 of 33)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [A Survey of LLM-based Agents in Medicine: How far are we from Baymax?](https://mlsanity.com/paper/2502.11211/) (2025)
- [Do as We Do, Not as You Think: the Conformity of Large Language Models](https://mlsanity.com/paper/2501.13381/) (2025)
- [VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge](https://mlsanity.com/paper/2411.12915/) (2024)
- [PatientSim: A Persona-Driven Simulator for Realistic Doctor-Patient Interactions](https://mlsanity.com/paper/2505.17818/) (2025)
- [MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation](https://mlsanity.com/paper/2503.13856/) (2025)
- [AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening](https://mlsanity.com/paper/2504.02870/) (2025)
- [MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration](https://mlsanity.com/paper/2410.12532/) (2024)
- [Using Generative AI and Multi-Agents to Provide Automatic Feedback](https://mlsanity.com/paper/2411.07407/) (2024)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training](https://mlsanity.com/paper/2510.15859/) (2025)
- [Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System](https://mlsanity.com/paper/2412.06828/) (2024)
- [Emerging Cyber Attack Risks of Medical AI Agents](https://mlsanity.com/paper/2504.03759/) (2025)
- [A Multimodal Multi-Agent Framework for Radiology Report Generation](https://mlsanity.com/paper/2505.09787/) (2025)
- [MASA: LLM-Driven Multi-Agent Systems for Autoformalization](https://mlsanity.com/paper/2510.08988/) (2025)
- [ClinTutor-R1: Advancing Scalable and Robust One-to-Many Alignment in Clinical Socratic Education](https://mlsanity.com/paper/2512.05671/) (2025)
- [From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization](https://mlsanity.com/paper/2505.16832/) (2025)
- [Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making](https://mlsanity.com/paper/2508.05996/) (2025)
- [XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis](https://mlsanity.com/paper/2604.02695/) (2026)
- [Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing](https://mlsanity.com/paper/2409.16913/) (2024)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI](https://mlsanity.com/paper/2509.08151/) (2025)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answering](https://mlsanity.com/paper/2508.02841/) (2025)
- [AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering](https://mlsanity.com/paper/2510.02328/) (2025)
- [Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras](https://mlsanity.com/paper/2510.09230/) (2025)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [Expertise-aware Multi-LLM Recruitment and Collaboration for Medical Decision-Making](https://mlsanity.com/paper/2508.13754/) (2025)
- [MAGMA-Edu: Multi-Agent Generative Multimodal Framework for Text-Diagram Educational Question Generation](https://mlsanity.com/paper/2511.18714/) (2025)

## Abstract (arXiv)

Large language models (LLMs) have had a significant impact on diverse research domains, including medicine and healthcare. However, the potential of LLMs as copilots in medical education remains underexplored. Current AI-assisted educational tools are limited by their solitary learning approach and inability to simulate the multi-disciplinary and interactive nature of actual medical training. To address these limitations, we propose MEDCO (Medical EDucation COpilots), a novel multi-agent-based copilot system specially developed to emulate real-world medical training environments. MEDCO incorporates three primary agents: an agentic patient, an expert doctor, and a radiologist, facilitating a multi-modal and interactive learning environment. Our framework emphasizes the learning of proficient question-asking skills, multi-disciplinary collaboration, and peer discussions between students. Our experiments show that simulated virtual students who underwent training with MEDCO not only achieved substantial performance enhancements comparable to those of advanced models, but also demonstrated human-like learning behaviors and improvements, coupled with an increase in the number of learning samples. This work contributes to medical education by introducing a copilot that implements an interactive and collaborative learning approach. It also provides valuable insights into the effectiveness of AI-integrated training paradigms.