Understanding unrealized trial enrollments following patient-to-trial matching with large language models.

Claire T Verhagen¹, Grace Westerman², Haley Heaviland², Erin Lynch², Olivia Brandenburg³, Jennifer Adams-Patton³, Regina Schwind⁴, Yongwoo David Seo⁵, Callisia N Clarke⁶, Ben George⁷, Anai N Kothari⁸

¹Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: https://twitter.com/claireverhagen.

²Clinical Trials Office, Medical College of Wisconsin Cancer Center, Milwaukee, WI.

³Triomics Research, San Francisco, CA.

⁴Triomics Research, San Francisco, CA. Electronic address: https://twitter.com/gg_schwind.

⁵Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: https://twitter.com/DavidSeoMD.

⁶Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: https://twitter.com/DrCNClarke.

⁷Division of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI.

⁸Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI. Electronic address: akothari@mcw.edu.

PubMed DOI

Abstract

BACKGROUND: Clinical trials are essential for advancing cancer care, but identifying eligible patients in surgical clinics can be challenging due to the manual and time-consuming enrollment process. Artificial intelligence tools, such as large language models, have the potential to automate aspects of clinical trial matching. This study identified reasons why patients did not enroll in a clinical…

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