SROTAS IQ: An AI-Based Clinical Trial Matching Platform: A Validation Study in Breast Cancer

ObjectivesTo evaluate the performance of SROTAS IQ, a custom fine-tuned large language model (LLM), in automating clinical trial eligibility screening for breast cancer patients using synthetic data. MethodsTen breast cancer trials were selected across diverse treatment settings and molecular subtypes. Fifteen synthetic patient summaries per trial were generated, including realistic and enriched eligibility scenarios. Two independent oncologists assessed trial eligibility for each patient, establishing ground truth. SROTAS IQ LLM was evaluated against expert consensus using standard classification metrics. Time-to-verdict was measured to compare clinician effort with automated assessment. ResultsSROTAS IQ demonstrated strong concordance with expert assessments, achieving 90% or greater accuracy in 5 of 10 trials. Across 150 patient-trial evaluations, the model correctly classified 88% of overall eligibility decisions. Performance was highest in trials with moderate complexity and fewer nested criteria, while more intricate protocols showed reduced accuracy. The LLM consistently delivered rapid assessments (<0.5 minutes per patient), with explainable outputs that aligned with clinical reasoning. These findings underscore the models potential to support high-fidelity, scalable trial matching in oncology. ConclusionSROTAS IQ offers a promising approach to automating clinical trial matching in oncology. Further real-world validation is needed to confirm generalisability and integration into clinical practice.