biostudies-literatureUnknown49(1)Habib DRSWhile artificial intelligence (AI) has demonstrated potential in automating clinical trial matching, most existing solutions rely on high-level structured data or oversimplified criteria. This study introduces a framework to structure and analyze eligibility criteria across three real-world trial protocols, aiming to inform more granular AI-driven trial matching strategies. Trial criteria from three protocols were decomposed into individual variables and evaluated based on data type, scope, and dependency. Complexity was assessed using a novel formula incorporating the number of independent and dependent variables, alongside the Flesch-Kincaid reading grade level. Quantitative analysis explored variation across trials. Protocols contained between 22-160 eligibility variables, with 4-22% shJournal of medical systems168https://www.ebi.ac.uk/biostudies/studies/S-EPMC12640310biostudies-literatureComputational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.PMC12640310Evancha BHabib DRSFabbri DMahajan IMicheel CfalseComputational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.While artificial intelligence (AI) has demonstrated potential in automating clinical trial matching, most existing solutions rely on high-level structured data or oversimplified criteria. This study introduces a framework to structure and analyze eligibility criteria across three real-world trial protocols, aiming to inform more granular AI-driven trial matching strategies. Trial criteria from three protocols were decomposed into individual variables and evaluated based on data type, scope, and dependency. Complexity was assessed using a novel formula incorporating the number of independent and dependent variables, alongside the Flesch-Kincaid reading grade level. Quantitative analysis explored variation across trials. Protocols contained between 22-160 eligibility variables, with 4-22% sh2025-01-01T00:00:00Z2025 Nov2026-06-05T18:47:23.073Z2026-05-20T03:13:50.415ZS-EPMC126403104127348410.1007/s10916-025-02303-y