Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
Year: 2026
Type: preprint
Abstract: Clinical trials are essential for evidence-based medicine and drug development, but many fail because of insufficient patient enrollment. Although there is growing interest in using AI to support patient recruitment, existing systems largely perform eligibility assessment alone and have rarely been evaluated in real-world workflows. Here we present TrialGPT 2.0, an AI-assisted clinical trial recommendation system improved for responsible real-world deployment. Rather than asking only whether a patient may qualify, the system assesses which trials warrant further consideration given the patient's current clinical needs and local workflow priorities, and provides structured, inspectable explanations for expert review. Importantly, we evaluated TrialGPT 2.0 retrospectively and prospectively across government research, patient-advocacy, academic medical center, and community-based radiation-oncology settings. In retrospective multicenter cohorts comprising 288 cases, TrialGPT 2.0 retrieved at least one clinician-recommended trial in its top 10 recommendations for approximately 91% of cases while reducing clinician screening time by 55.0%. In a six-month prospective evaluation embedded in an active precision oncology tumor board, TrialGPT 2.0 contributed additional trial opportunities missed by the routine workflow, increasing the number of patient cases with at least one final trial recommendation by 90.9%. To support scientific reproducibility, we also developed NIH-TrialBench, a clinician-authored benchmark comprising 126 diverse synthetic patient vignettes and matching scenarios from 11 NIH Institutes and Centers. Together, these findings support the value of AI to assist clinical trial matching by improving clinician efficiency and identifying frequently overlooked trial opportunities, ultimately helping to expand and accelerate accrual to cancer trials.
Source: arXiv (Cornell University)
Authors: Fang, Yin, Jin, Qiao, Tian, Shubo, He, Lauren, Geer, Maya, Naffakh, Noor, Nguyen, Ryan Huu-Tuan, Wang, Zifeng, Sun, Jimeng, Floudas, Charalampos S., Gulley, James L., Moalem, Kamilia, Maia, Catarina Martins, Nottke, Amanda, Valle, Juan W., Bachini, Melinda, Rocha-Nussbaum, Lourdes, Ramage, Kari, Curry, Nikita, Barnes, Megan, Mansaray, Mandy, Gabeau, Darlene, Grossman, Craig E., Skinner, Heath, Burczynski, Michael, Consortium, NIH-TrialBench, Lu, Zhiyong
Institutions: National Institutes of Health, United States National Library of Medicine, University of Illinois Chicago, University of Illinois Hospital & Health Sciences System, University of Illinois Urbana-Champaign, National Cancer Institute, Center for Cancer Research, Cholangiocarcinoma Foundation, University of Manchester, National Institutes of Health Clinical Center, University of Pittsburgh, UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, National Institute of Allergy and Infectious Diseases
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
Award: G7459493912
OpenAlex ID: W7206158180