AI多代理系统可模拟专家肿瘤会诊，提升资源匮乏地区卵巢癌诊疗水平。
OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum
- 构建多代理框架，协同整合多学科证据生成可解释推荐
- 在多中心评估中表现接近专家会诊（4.45比4.53），证据评分更高（4.57比3.92）
- 尤其增强临床医生在证据与稳健性上的判断，适合医疗资源不足地区
卵巢肿瘤管理日益依赖多学科肿瘤委员会（MDT）讨论以应对治疗复杂性和疾病异质性。然而，全球多数患者无法及时获得专家共识，尤其在资源匮乏的医疗机构中，MDT资源稀缺或缺失。本文提出OMGs（卵巢肿瘤多学科智能代理系统），一种多代理人工智能框架，通过领域专用代理协作，整合多学科证据并生成具有透明推理过程的MDT式建议。为系统评估MDT建议质量，我们开发了SPEAR（安全性、个性化、证据性、可操作性、鲁棒性）评估体系，并在覆盖诊疗全流程的多样化临床场景中验证OMGs。在多中心回顾性评估中，OMGs性能与专家MDT共识相当（4.45 ± 0.30 对比 4.53 ± 0.23），证据评分显著更高（4.57 对比 3.92）。在前瞻性多中心研究（59例患者）中，OMGs与常规MDT决策高度一致。关键的是，在人机对比研究中，OMGs在证据性和鲁棒性维度上对临床医生建议提升最显著，而这两项正是多学科专家缺失时最易削弱的方面。结果表明，多代理协商系统可达到与专家MDT共识相当的水平，有望拓展资源有限地区专业肿瘤学支持的可及性。
原文摘要 · Abstract (English)
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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