medRxiv
Quantifying Cancer Clinical Trial Eligibility Using Artificial Intelligence-Based Matching
By Goel, K. P., Myall, N. J., Dickerson, J. and 4 others.
Abstract
Purpose
To develop and validate an artificial intelligence-enabled platform that combines efficient screening with explicit eligibility logic by converting unstructured cancer trial criteria into reusable structured queries, and illustrates potential eligibility using synthetic patients.
Methods
We downloaded actively recruiting US interventional treatment trials for advanced/metastatic breast cancer, colon cancer, and non-small cell lung cancer from ClinicalTrials.gov. Medical oncologists created 24 synthetic patient vignettes. A large language model converted trial eligibility criteria into Structured Query Language (SQL) code and patient information into structured records, enabling rule-based matching without further LLM inference. Cancer details and treatment history were considered, but not laboratory results or comorbidities. Validation included physician editing of generated eligibility code for 30 trials and independent assessments by two physicians, followed by consensus adjudication, for eligibility across ten trials. We descriptively summarized potential eligibility by trial characteristics and when varying age, ECOG performance status, sex, and ZIP code.
