Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent

Matching patients effectively and efficiently for clinical trials is a significant challenge due to the complexity and variability of patient profiles and trial criteria. This paper introduces $\mathbf{\text{Multi-Agent}}\mathbf{\text{for}}\mathbf{\text{Knowledge}}\mathbf{\text{Augmentation}}\mathbf{\text{and}}\mathbf{\text{Reasoning}}\mathbf{\text{(MAKAR)}}$, a novel multi-agent system that enhances patient-trial matching by integrating criterion augmentation with structured reasoning. MAKAR consistently improves performance by an average of 7% across different datasets. Furthermore, it enables privacy-preserving deployment and maintains competitive performance when using smaller open-source models. Overall, MAKAR can contributes to more transparent, accurate, and privacy-conscious AI-driven patient matching.