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

• URL: http://arxiv.org/abs/2410.00046v3
• Date: Thu, 18 Sep 2025 15:48:24 GMT
• Title: Mixture of Multicenter Experts in Multimodal AI for Debiased Radiotherapy Target Delineation
• Authors: Yujin Oh, Sangjoon Park, Xiang Li, Pengfei Jin, Yi Wang, Jonathan Paly, Jason Efstathiou, Annie Chan, Jun Won Kim, Hwa Kyung Byun, Ik Jae Lee, Jaeho Cho, Chan Woo Wee, Peng Shu, Peilong Wang, Nathan Yu, Jason Holmes, Jong Chul Ye, Quanzheng Li, Wei Liu, Woong Sub Koom, Jin Sung Kim, Kyungsang Kim,
• Abstract summary: We propose a Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions.<n>MoME integrates specialized expertise from diverse clinical strategies to enhance model generalizability and adaptability across medical centers.
• Score: 40.85439754751206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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