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Computational Framework for Structuring and Analyzing Clinical Trial Criteria for AI-Guided Fine-grained Matching.

Daniel R S Habib¹, Ishan Mahajan², Betina Evancha¹

¹Vanderbilt University School of Medicine, Nashville, TN, USA.

Journal of Medical Systems

|November 22, 2025

Summary

Artificial intelligence (AI) for clinical trial matching needs more than simple data. This study developed a framework to analyze complex trial criteria, improving AI accuracy and patient access.

Keywords:

Artificial intelligence Clinical Decision support systems Eligibility determination Medical informatics Natural language processing

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Area of Science:

Clinical Informatics
Artificial Intelligence in Healthcare
Clinical Trial Management

Background:

Current artificial intelligence (AI) solutions for clinical trial matching often use oversimplified criteria or limited structured data.
This limitation hinders the potential of AI to effectively automate and optimize patient recruitment for clinical trials.
Existing methods struggle with the inherent complexity and variability of clinical trial eligibility criteria.

Purpose of the Study:

To introduce a novel framework for structuring and analyzing eligibility criteria from real-world clinical trial protocols.
To inform the development of more granular and effective AI-driven clinical trial matching strategies.
To quantify the complexity and variability of clinical trial eligibility criteria.

Main Methods:

Eligibility criteria from three clinical trial protocols were systematically decomposed into individual variables.
Variables were evaluated based on data type, scope, and interdependencies.
A novel complexity formula, incorporating variable interdependence and Flesch-Kincaid reading grade level, was developed and applied.

Main Results:

Clinical trial protocols contained between 22 and 160 eligibility variables, with 4-22% exhibiting interdependence.
Reading grade levels for criteria ranged from sixth grade to first-year college, indicating significant accessibility variation.
Complexity scores varied substantially, with high cognitive and logical burdens observed in some protocols, often featuring recursive and hierarchical structures.

Conclusions:

Clinical trial eligibility criteria exhibit significant variability and structural complexity, posing challenges for current AI matching systems.
A standardized method for assessing trial complexity can enhance AI algorithm transparency, scalability, and interpretability.
Developing structured, computable frameworks is crucial for improving the equity and efficiency of clinical trial recruitment.