TrialGPT 2.0 论文提出 AI 辅助临床试验匹配并发布 NIH-TrialBench 基准
Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
NIH 团队发布 TrialGPT 2.0，用于 AI 辅助临床试验匹配，并配套构建含 126 个患者病例场景、覆盖 11 个 NIH 中心的 NIH-TrialBench 基准。在 288 例回顾性多中心队列中，系统前 10 推荐约 91% 的病例命中临床医生推荐试验，临床医生筛查时间减少 55.0%；在六个月前瞻性肿瘤板评估中，将至少获得一条最终试验推荐的患者病例数提升 90.9%。
论文给出多中心回顾与前瞻数据，还开源了临床医生编写的基准，可帮助读者了解 AI 试验匹配在真实流程中的效果。
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
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.
| 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:2609.01202v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01202 arXiv-issued DOI via DataCite |
Submission history
From: Yin Fang [view email]
[v1]
Tue, 1 Sep 2026 13:11:02 UTC (22,625 KB)
[v2]
Wed, 28 Oct 2026 19:46:44 UTC (23,275 KB)
来源：arXiv cs.AI · arxiv.org