OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918338361294848 |
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| author | Zhang, Yangyang Wang, Zilong Xu, Jianbo Chen, Yongqi Han, Chu Zhang, Zhihao Liu, Shuai Li, Hui Zhang, Huiping Liu, Ziqi Chen, Jiaxin Zhu, Jun Feng, Zheng Wen, Hao Ju, Xingzhu Zhong, Yanping Zhang, Yunqiu Duan, Jie Li, Jun Li, Dongsheng Wang, Weijie Zhu, Haiyan Jiang, Wei Wu, Xiaohua Wang, Shuo Li, Haiming Guo, Qinhao |
| author_facet | Zhang, Yangyang Wang, Zilong Xu, Jianbo Chen, Yongqi Han, Chu Zhang, Zhihao Liu, Shuai Li, Hui Zhang, Huiping Liu, Ziqi Chen, Jiaxin Zhu, Jun Feng, Zheng Wen, Hao Ju, Xingzhu Zhong, Yanping Zhang, Yunqiu Duan, Jie Li, Jun Li, Dongsheng Wang, Weijie Zhu, Haiyan Jiang, Wei Wu, Xiaohua Wang, Shuo Li, Haiming Guo, Qinhao |
| contents | Ovarian tumour management has increasingly relied on multidisciplinary tumour board (MDT) deliberation to address treatment complexity and disease heterogeneity. However, most patients worldwide lack access to timely expert consensus, particularly in resource-constrained centres where MDT resources are scarce or unavailable. Here we present OMGs (Ovarian tumour Multidisciplinary intelligent aGent System), a multi-agent AI framework where domain-specific agents deliberate collaboratively to integrate multidisciplinary evidence and generate MDT-style recommendations with transparent rationales. To systematically evaluate MDT recommendation quality, we developed SPEAR (Safety, Personalization, Evidence, Actionability, Robustness) and validated OMGs across diverse clinical scenarios spanning the care continuum. In multicentre re-evaluation, OMGs achieved performance comparable to expert MDT consensus ($4.45 \pm 0.30$ versus $4.53 \pm 0.23$), with higher Evidence scores (4.57 versus 3.92). In prospective multicentre evaluation (59 patients), OMGs demonstrated high concordance with routine MDT decisions. Critically, in paired human-AI studies, OMGs most substantially enhanced clinicians' recommendations in Evidence and Robustness, the dimensions most compromised when multidisciplinary expertise is unavailable. These findings suggest that multi-agent deliberative systems can achieve performance comparable to expert MDT consensus, with potential to expand access to specialized oncology expertise in resource-limited settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13793 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum Zhang, Yangyang Wang, Zilong Xu, Jianbo Chen, Yongqi Han, Chu Zhang, Zhihao Liu, Shuai Li, Hui Zhang, Huiping Liu, Ziqi Chen, Jiaxin Zhu, Jun Feng, Zheng Wen, Hao Ju, Xingzhu Zhong, Yanping Zhang, Yunqiu Duan, Jie Li, Jun Li, Dongsheng Wang, Weijie Zhu, Haiyan Jiang, Wei Wu, Xiaohua Wang, Shuo Li, Haiming Guo, Qinhao Computation and Language Ovarian tumour management has increasingly relied on multidisciplinary tumour board (MDT) deliberation to address treatment complexity and disease heterogeneity. However, most patients worldwide lack access to timely expert consensus, particularly in resource-constrained centres where MDT resources are scarce or unavailable. Here we present OMGs (Ovarian tumour Multidisciplinary intelligent aGent System), a multi-agent AI framework where domain-specific agents deliberate collaboratively to integrate multidisciplinary evidence and generate MDT-style recommendations with transparent rationales. To systematically evaluate MDT recommendation quality, we developed SPEAR (Safety, Personalization, Evidence, Actionability, Robustness) and validated OMGs across diverse clinical scenarios spanning the care continuum. In multicentre re-evaluation, OMGs achieved performance comparable to expert MDT consensus ($4.45 \pm 0.30$ versus $4.53 \pm 0.23$), with higher Evidence scores (4.57 versus 3.92). In prospective multicentre evaluation (59 patients), OMGs demonstrated high concordance with routine MDT decisions. Critically, in paired human-AI studies, OMGs most substantially enhanced clinicians' recommendations in Evidence and Robustness, the dimensions most compromised when multidisciplinary expertise is unavailable. These findings suggest that multi-agent deliberative systems can achieve performance comparable to expert MDT consensus, with potential to expand access to specialized oncology expertise in resource-limited settings. |
| title | OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.13793 |