MedTrialMatch: An AI-Powered Clinical Trial Eligibility Prediction System Using NLP

Clinical trial participation plays a crucial role in advancing medical research and developing innovative treatment strategies. However, identifying eligible patients for suitable clinical trials based on medical reports remains a complex and manual process. This paper presents MedTrialMatch, an AI-powered clinical trial eligibility prediction system that analyzes multimodal medical data including medical imaging reports, laboratory results, diagnostic summaries, and prescriptions to predict disease conditions and recommend relevant clinical trials.

The proposed system integrates document intelligence, Natural Language Processing (NLP), deep learning-based feature extraction, and ensemble machine learning classification. The current implementation includes a frontend prototype, backend processing using Flask, and MongoDB database integration. Experimental evaluation on simulated healthcare datasets demonstrates strong predictive performance, achieving 94% accuracy, 92% precision, 91% recall, 91.5% F1-score, and 95% ROC-AUC. The system is scalable for future integration with real hospital Electronic Health Records (EHR) systems and deep learning models for advanced medical analysis.