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

• URL: http://arxiv.org/abs/2512.08026v1
• Date: Mon, 08 Dec 2025 20:35:51 GMT
• Title: Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching
• Authors: Caroline N. Leach, Mitchell A. Klusty, Samuel E. Armstrong, Justine C. Pickarski, Kristen L. Hankins, Emily B. Collier, Maya Shah, Aaron D. Mullen, V. K. Cody Bumgardner,
• Abstract summary: We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching.<n>The system generates structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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