Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909974325624832 |
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| author | Zhang, Rongzhao Wang, Junqiao Yang, Shuyun Bian, Mouxiao Zhang, Chihao Wang, Dongyang Yan, Qiujuan Zhong, Yun Bai, Yuwei Zhu, Guanxu Mao, Kangkun Wang, Miao Ding, Chao Lu, Renjie Wang, Lei Zheng, Lei Zheng, Tao Wang, Xi Fan, Zhuo Han, Bing Liu, Meiling Jiang, Luyi Shan, Dongming Jin, Wenzhong Yu, Jiwei Wang, Zheng Xu, Jie Luo, Meng |
| author_facet | Zhang, Rongzhao Wang, Junqiao Yang, Shuyun Bian, Mouxiao Zhang, Chihao Wang, Dongyang Yan, Qiujuan Zhong, Yun Bai, Yuwei Zhu, Guanxu Mao, Kangkun Wang, Miao Ding, Chao Lu, Renjie Wang, Lei Zheng, Lei Zheng, Tao Wang, Xi Fan, Zhuo Han, Bing Liu, Meiling Jiang, Luyi Shan, Dongming Jin, Wenzhong Yu, Jiwei Wang, Zheng Xu, Jie Luo, Meng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08674 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology Zhang, Rongzhao Wang, Junqiao Yang, Shuyun Bian, Mouxiao Zhang, Chihao Wang, Dongyang Yan, Qiujuan Zhong, Yun Bai, Yuwei Zhu, Guanxu Mao, Kangkun Wang, Miao Ding, Chao Lu, Renjie Wang, Lei Zheng, Lei Zheng, Tao Wang, Xi Fan, Zhuo Han, Bing Liu, Meiling Jiang, Luyi Shan, Dongming Jin, Wenzhong Yu, Jiwei Wang, Zheng Xu, Jie Luo, Meng Artificial Intelligence Multiagent Systems 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. |
| title | Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2512.08674 |