# MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis

arXiv: https://arxiv.org/abs/2502.19175 · HTML: https://mlsanity.com/paper/2502.19175/

## Key facts

- Cited by 23 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 25 of its 55 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## 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, 25 of 25)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (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)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents](https://mlsanity.com/paper/2405.02957/) (2024)
- [MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://mlsanity.com/paper/2406.00922/) (2024)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Towards Building Multilingual Language Model for Medicine](https://mlsanity.com/paper/2402.13963/) (2024)
- [DDXPlus: A New Dataset For Automatic Medical Diagnosis](https://mlsanity.com/paper/2205.09148/) (2022)
- [Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine](https://mlsanity.com/paper/2308.06834/) (2023)
- [Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings](https://mlsanity.com/paper/2305.02317/) (2023)
- [UltraMedical: Building Specialized Generalists in Biomedicine](https://mlsanity.com/paper/2406.03949/) (2024)
- [StreamBench: Towards Benchmarking Continuous Improvement of Language Agents](https://mlsanity.com/paper/2406.08747/) (2024)
- [RareBench: Can LLMs Serve as Rare Diseases Specialists?](https://mlsanity.com/paper/2402.06341/) (2024)
- [CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis](https://mlsanity.com/paper/2407.13301/) (2024)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)
- [AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents](https://mlsanity.com/paper/2404.06411/) (2024)
- [KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis](https://mlsanity.com/paper/2412.16833/) (2024)
- [Large Language Models Perform Diagnostic Reasoning](https://mlsanity.com/paper/2307.08922/) (2023)
- [DDxT: Deep Generative Transformer Models for Differential Diagnosis](https://mlsanity.com/paper/2312.01242/) (2023)

## Built on by (most cited first, 23 of 23)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [MedKGI: Iterative Differential Diagnosis with Medical Knowledge Graphs and Information-Guided Inquiring](https://mlsanity.com/paper/2512.24181/) (2025)
- [MeDxAgent: Multi-Agent Consultation for Interactive Medical Diagnosis](https://mlsanity.com/paper/2606.03416/) (2026)
- [Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation](https://mlsanity.com/paper/2604.00131/) (2026)
- [MediSkill-Evo: Process-Constrained Self-Evolution for Evidence-Grounded Clinical Interaction](https://mlsanity.com/paper/2608.23397/) (2026)
- [DDO: Dual-Decision Optimization for LLM-Based Medical Consultation via Multi-Agent Collaboration](https://mlsanity.com/paper/2505.18630/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes](https://mlsanity.com/paper/2507.12261/) (2025)
- [Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes](https://mlsanity.com/paper/2601.21551/) (2026)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [MIND: Unified Inquiry and Diagnosis RL with Criteria Grounded Clinical Supports for Psychiatric Consultation](https://mlsanity.com/paper/2603.03677/) (2026)
- [From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department](https://mlsanity.com/paper/2608.23627/) (2026)
- ["Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues](https://mlsanity.com/paper/2508.19077/) (2025)
- [GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations](https://mlsanity.com/paper/2510.03536/) (2025)
- [Process-Supervised Multi-Agent Reinforcement Learning for Reliable Clinical Reasoning](https://mlsanity.com/paper/2602.14160/) (2026)
- [MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis](https://mlsanity.com/paper/2603.01131/) (2026)
- [MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning](https://mlsanity.com/paper/2604.24186/) (2026)
- [Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral](https://mlsanity.com/paper/2605.31512/) (2026)
- [CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents](https://mlsanity.com/paper/2608.22899/) (2026)
- [From Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis](https://mlsanity.com/paper/2608.27847/) (2026)
- [KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy](https://mlsanity.com/paper/2609.38480/) (2026)

## Abstract (arXiv)

Differential Diagnosis (DDx) is a fundamental yet complex aspect of clinical decision-making, in which physicians iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. While recent advances in large language models (LLMs) have shown promise in supporting DDx, existing approaches face key limitations, including single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. We introduce a Modular Explainable DDx Agent (MEDDxAgent) framework designed for interactive DDx, where diagnostic reasoning evolves through iterative learning, rather than assuming a complete patient profile is accessible. MEDDxAgent integrates three modular components: (1) an orchestrator (DDxDriver), (2) a history taking simulator, and (3) two specialized agents for knowledge retrieval and diagnosis strategy. To ensure robust evaluation, we introduce a comprehensive DDx benchmark covering respiratory, skin, and rare diseases. We analyze single-turn diagnostic approaches and demonstrate the importance of iterative refinement when patient profiles are not available at the outset. Our broad evaluation demonstrates that MEDDxAgent achieves over 10% accuracy improvements in interactive DDx across both large and small LLMs, while offering critical explainability into its diagnostic reasoning process.