Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 14 references that are in the map. First posted on arXiv 9 Dec 2025.
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References · 8
- 2025Qwen3 Technical Reportcited by 8,195
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- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
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- 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 311
- 2023Towards Generalist Biomedical AIcited by 286
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- 2025Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning
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