MMedAgent: Learning to Use Medical Tools with Multi-modal Agent
On the map: Multimodal LLMs / vision-language models → Medical vision-language models
also in 1 more
Abstract (arXiv)
Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited generality and often fall short when compared to specialized models. Recently, LLM-based agents have been developed to address these challenges by selecting appropriate specialized models as tools based on user inputs. However, such advancements have not been extensively explored within the medical domain. To bridge this gap, this paper introduces the first agent explicitly designed for the medical field, named Multi-modal Medical Agent (MMedAgent). We curate an instruction-tuning dataset comprising six medical tools solving seven tasks across five modalities, enabling the agent to choose the most suitable tools for a given task. Comprehensive experiments demonstrate that MMedAgent achieves superior performance across a variety of medical tasks compared to state-of-the-art open-source methods and even the closed-source model, GPT-4o. Furthermore, MMedAgent exhibits efficiency in updating and integrating new medical tools. Codes and models are all available.
Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 34 of its 56 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
34 of 56 references.
Closest ancestors · 2
Same direction, newest first
- 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun
- 2023PathAsst: A Generative Foundation AI Assistant Towards Artificial General Intelligence of Pathology24 May
Other references · 32
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- 2023Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editingcited by 15
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Citing papers per year: 2024: 11; 2025: 47; 2026: 50.
108 papers.
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- 2026CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays26 Feb 2026
- 2026Process-Supervised Multi-Agent Reinforcement Learning for Reliable Clinical Reasoning15 Feb 2026
- 2026MedScope: Incentivizing "Think with Videos" for Clinical Reasoning via Coarse-to-Fine Tool Calling11 Feb 2026
- 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents18 Jan 2026
- 2026MEDVISTAGYM: A Scalable Training Environment for Thinking with Medical Images via Tool-Integrated Reinforcement Learning12 Jan 2026
- 2026IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation6 Jan 2026
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- 2025CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decodingcited by 4
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- 2025Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making15 Dec 2025
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- 2025AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering26 Sep 2025
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- 2024A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine1 Sep 2024
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