Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
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Computer Science > Computation and Language
arXiv:2609.01202 (cs)
[Submitted on 1 Sep 2026]
Title:Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
Authors:Yin Fang, Qiao Jin, Shubo Tian, Lauren He, Maya Geer, Noor Naffakh, Ryan Huu-Tuan Nguyen, Zifeng Wang, Jimeng Sun, Charalampos S. Floudas, James L. Gulley, Kamilia Moalem, Catarina Martins Maia, Amanda Nottke, Juan W. Valle, Melinda Bachini, Lourdes Rocha-Nussbaum, Kari Ramage, Nikita Curry, Megan Barnes, Mandy Mansaray, Darlene Gabeau, Craig E. Grossman, Heath Skinner, Michael Burczynski, NIH-TrialBench Consortium, Zhiyong Lu
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Abstract:Clinical trials are essential for advancing cancer care and drug development, but many fail because of insufficient patient enrollment. While 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 oncology workflows. Here we present TrialGPT 2.0, an AI-assisted clinical trial recommendation system designed for real-world deployment. Rather than asking only whether a patient may qualify, the system also 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 multiple oncology-focused settings, spanning government, academic cancer-center, patient-advocacy, and NIH referral workflows. 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, expanding patient access to clinical trial participation by 90.9%. To support scientific reproducibility, we also introduce NIH-TrialBench, a clinician-authored dataset comprising 126 diverse synthetic patient vignettes and matching scenarios from 11 NIH Institutes and Centers. Together, these results 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.
| Comments: | 43 pages, 12 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2609.01202 [cs.CL] |
| (or arXiv:2609.01202v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01202
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment, by Yin Fang and 26 other authors
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