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

TL;DR

This paper introduces OMGs, a multi-agent AI system that supports ovarian tumour treatment decisions, matching expert MDT performance and improving evidence and robustness in resource-limited settings.

Contribution

The paper presents OMGs, a novel multi-agent AI framework for MDT decision support in ovarian tumour care, validated across multiple clinical scenarios and settings.

Findings

OMGs achieved expert-level MDT recommendation performance.

OMGs showed higher Evidence scores than traditional MDTs.

In resource-limited settings, OMGs enhanced clinician recommendations.

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.