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arXiv · 2511.05696

AI-assisted workflow enables rapid, high-fidelity breast cancer clinical trial eligibility prescreening

Jacob T. Rosenthal·Emma Hahesy·Sulov Chalise·Menglei Zhu·Mert R. Sabuncu·Lior Z. Braunstein·Anyi Li
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Abstract

Clinical trials play an important role in cancer care and research, yet participation rates remain low. We developed MSK-MATCH (Memorial Sloan Kettering Multi-Agent Trial Coordination Hub), an AI system for automated eligibility screening from clinical text. MSK-MATCH integrates a large language model with a curated oncology trial knowledge base and retrieval-augmented architecture providing explanations for all AI predictions grounded in source text. In a retrospective dataset of 88,518 clinical documents from 731 patients across six breast cancer trials, MSK-MATCH automatically resolved 61.9% of cases and triaged 38.1% for human review. This AI-assisted workflow achieved 98.6% accuracy, 98.4% sensitivity, and 98.7% specificity for patient-level eligibility classification, matching or exceeding performance of the human-only and AI-only comparisons. For the triaged cases requiring manual review, prepopulating eligibility screens with AI-generated explanations reduced screening time from 20 minutes to 43 seconds at an average cost of $0.96 per patient-trial pair.

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Jacob T. Rosenthal, Emma Hahesy, Sulov Chalise, Menglei Zhu, Mert R. Sabuncu, Lior Z. Braunstein, Anyi Li. 2025-11-07. AI-assisted workflow enables rapid, high-fidelity breast cancer clinical trial eligibility prescreening. https://arxiv.org/abs/2511.05696

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