Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework
This addresses the need for interpretable and accurate eGFR forecasting in clinical settings, though it appears incremental by enhancing existing LMMs rather than introducing a new paradigm.
The study tackled the problem of predicting estimated glomerular filtration rate (eGFR) for chronic kidney disease management by proposing a collaborative framework using open-source Large Multimodal Models (LMMs), achieving predictive performance and interpretability comparable to proprietary models.
Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recent advances in Large Multimodal Models (LMMs) have shown strong potential in clinical prediction tasks due to their ability to process visual and textual information. However, challenges related to deployment cost, data privacy, and model reliability hinder their adoption. In this study, we propose a collaborative framework that enhances the performance of open-source LMMs for eGFR forecasting while generating clinically meaningful explanations. The framework incorporates visual knowledge transfer, abductive reasoning, and a short-term memory mechanism to enhance prediction accuracy and interpretability. Experimental results show that the proposed framework achieves predictive performance and interpretability comparable to proprietary models. It also provides plausible clinical reasoning processes behind each prediction. Our method sheds new light on building AI systems for healthcare that combine predictive accuracy with clinically grounded interpretability.