OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum

• URL: http://arxiv.org/abs/2602.13793v1
• Date: Sat, 14 Feb 2026 14:13:10 GMT
• Title: OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum
• Authors: Yangyang Zhang, Zilong Wang, Jianbo Xu, Yongqi Chen, Chu Han, Zhihao Zhang, Shuai Liu, Hui Li, Huiping Zhang, Ziqi Liu, Jiaxin Chen, Jun Zhu, Zheng Feng, Hao Wen, Xingzhu Ju, Yanping Zhong, Yunqiu Zhang, Jie Duan, Jun Li, Dongsheng Li, Weijie Wang, Haiyan Zhu, Wei Jiang, Xiaohua Wu, Shuo Wang, Haiming Li, Qinhao Guo,
• Abstract summary: Ovarian tumour management has increasingly relied on multidisciplinary tumour board (MDT) deliberation.<n>Most patients worldwide lack access to timely expert consensus.<n>Here we present OMGs (Ovarian tumour Multidisciplinary intelligent aGent System), a multi-agent AI framework.
• Score: 51.97232679580821
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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