Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation

TL;DR

This paper introduces a Mixture of Multicenter Experts framework that leverages diverse clinical expertise to improve the generalizability and adaptability of medical AI models in radiotherapy, especially in data-limited and high-variability settings.

Contribution

It proposes a novel MoME framework that enhances medical AI robustness without requiring data sharing across institutions.

Findings

Outperforms baseline models in inter-center variability scenarios

Enables local model customization without data exchange

Effective with few-shot training using multimodal data

Abstract

Clinical decision-making reflects diverse strategies shaped by regional patient populations and institutional protocols. However, most existing medical artificial intelligence (AI) models are trained on highly prevalent data patterns, which reinforces biases and fails to capture the breadth of clinical expertise. Inspired by the recent advances in Mixture of Experts (MoE), we propose a Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions. MoME integrates specialized expertise from diverse clinical strategies to enhance model generalizability and adaptability across medical centers. We validate this framework using a multimodal target volume delineation model for prostate cancer radiotherapy. With few-shot training that combines imaging and clinical notes from each center, the model outperformed baselines, particularly in settings with high inter-center variability or limited data availability. Furthermore, MoME enables model customization to local clinical preferences without cross-institutional data exchange, making it especially suitable for resource-constrained settings while promoting broadly generalizable medical AI.