Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching

TL;DR

This paper introduces a secure, scalable AI-powered system that enhances patient-clinical trial matching by generating interpretable eligibility assessments, supporting expert review, and maintaining rigorous security standards to improve efficiency and accuracy.

Contribution

It presents a novel AI system using reasoning-enabled large language models for dynamic, interpretable patient-trial matching with human-in-the-loop review and security features.

Findings

Supports human review with interpretable reasoning chains

Identifies potential future eligibility for patients

Reduces manual screening burden

Abstract

Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.