# MedAide: Information Fusion and Anatomy of Medical Intents via LLM-based Agent Collaboration

arXiv: https://arxiv.org/abs/2410.12532 · HTML: https://mlsanity.com/paper/2410.12532/

## Key facts

- Cited by 16 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 21 of its 43 references that are in the map.
- First posted on arXiv 16 Oct 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, 21 of 21)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue](https://mlsanity.com/paper/2308.03549/) (2023)
- [AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://mlsanity.com/paper/2402.09742/) (2024)
- [HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs](https://mlsanity.com/paper/2311.09774/) (2023)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom](https://mlsanity.com/paper/2306.11963/) (2023)
- [COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal](https://mlsanity.com/paper/2312.11792/) (2023)
- [PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications](https://mlsanity.com/paper/2405.19266/) (2024)
- [Ziya2: Data-centric Learning is All LLMs Need](https://mlsanity.com/paper/2311.03301/) (2023)
- [Distilling Implicit Multimodal Knowledge into Large Language Models for Zero-Resource Dialogue Generation](https://mlsanity.com/paper/2405.10121/) (2024)

## Built on by (most cited first, 16 of 16)

- [Resolving Evidence Sparsity: Agentic Context Engineering for Long-Document Understanding](https://mlsanity.com/paper/2511.22850/) (2025)
- [Position: The Real Barrier to LLM Agent Usability is Agentic ROI](https://mlsanity.com/paper/2505.17767/) (2025)
- [Multi-step retrieval and reasoning improves radiology question answering with large language models](https://mlsanity.com/paper/2508.00743/) (2025)
- [Reasoning LLMs in the Medical Domain: A Literature Survey](https://mlsanity.com/paper/2508.19097/) (2025)
- [BioVerge: A Comprehensive Benchmark and Study of Self-Evaluating Agents for Biomedical Hypothesis Generation](https://mlsanity.com/paper/2511.08866/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Surgical AI Copilot: Energy-Based Fourier Gradient Low-Rank Adaptation for Surgical LLM Agent Reasoning and Planning](https://mlsanity.com/paper/2503.09474/) (2025)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [MedProbeBench: Systematic Benchmarking at Deep Evidence Integration for Expert-level Medical Guideline](https://mlsanity.com/paper/2604.18418/) (2026)
- [Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning](https://mlsanity.com/paper/2607.07761/) (2026)
- [Process-Supervised Multi-Agent Reinforcement Learning for Reliable Clinical Reasoning](https://mlsanity.com/paper/2602.14160/) (2026)
- [MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus](https://mlsanity.com/paper/2603.05129/) (2026)
- [Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection](https://mlsanity.com/paper/2603.26745/) (2026)
- [MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning](https://mlsanity.com/paper/2604.24186/) (2026)
- [OmniFysics-Captioner Technical Report: Grounding Omni-Modal Understanding in the Physical World for Better Captioning](https://mlsanity.com/paper/2609.31714/) (2026)
- [Fysiverse-3D-SimReady Technical Report: Agentic Physical Simulation for Pragmatic 3D World Reconstruction](https://mlsanity.com/paper/2609.31715/) (2026)

## Abstract (arXiv)

In healthcare intelligence, the ability to fuse heterogeneous, multi-intent information from diverse clinical sources is fundamental to building reliable decision-making systems. Large Language Model (LLM)-driven information interaction systems currently showing potential promise in the healthcare domain. Nevertheless, they often suffer from information redundancy and coupling when dealing with complex medical intents, leading to severe hallucinations and performance bottlenecks. To this end, we propose MedAide, an LLM-based medical multi-agent collaboration framework designed to enable intent-aware information fusion and coordinated reasoning across specialized healthcare domains. Specifically, we introduce a regularization-guided module that combines syntactic constraints with retrieval augmented generation to decompose complex queries into structured representations, facilitating fine-grained clinical information fusion and intent resolution. Additionally, a dynamic intent prototype matching module is proposed to utilize dynamic prototype representation with a semantic similarity matching mechanism to achieve adaptive recognition and updating of the agent's intent in multi-round healthcare dialogues. Ultimately, we design a rotation agent collaboration mechanism that introduces dynamic role rotation and decision-level information fusion across specialized medical agents. Extensive experiments are conducted on four medical benchmarks with composite intents. Experimental results from automated metrics and expert doctor evaluations show that MedAide outperforms current LLMs and improves their medical proficiency and strategic reasoning.