MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modelling

• URL: http://arxiv.org/abs/2508.05492v1
• Date: Thu, 07 Aug 2025 15:28:34 GMT
• Title: MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modelling
• Authors: Jifan Gao, Mahmudur Rahman, John Caskey, Madeline Oguss, Ann O'Rourke, Randy Brown, Anne Stey, Anoop Mayampurath, Matthew M. Churpek, Guanhua Chen, Majid Afshar,
• Abstract summary: We introduce a novel architecture, Mixture-of-Multimodal-Agents (MoMA), designed to leverage multiple large language model (LLM) agents for clinical prediction tasks.<n>MoMA employs specialized LLM agents ("specialist agents") to convert non-textual modalities, such as medical images and laboratory results, into structured textual summaries.<n>MoMA outperforms current state-of-the-art methods, highlighting its enhanced accuracy and flexibility across various tasks.
• Score: 5.334856176687711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal electronic health record (EHR) data provide richer, complementary insights into patient health compared to single-modality data. However, effectively integrating diverse data modalities for clinical prediction modeling remains challenging due to the substantial data requirements. We introduce a novel architecture, Mixture-of-Multimodal-Agents (MoMA), designed to leverage multiple large language model (LLM) agents for clinical prediction tasks using multimodal EHR data. MoMA employs specialized LLM agents ("specialist agents") to convert non-textual modalities, such as medical images and laboratory results, into structured textual summaries. These summaries, together with clinical notes, are combined by another LLM ("aggregator agent") to generate a unified multimodal summary, which is then used by a third LLM ("predictor agent") to produce clinical predictions. Evaluating MoMA on three prediction tasks using real-world datasets with different modality combinations and prediction settings, MoMA outperforms current state-of-the-art methods, highlighting its enhanced accuracy and flexibility across various tasks.

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