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Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment — arXiv:2609.01202

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01202
License (as archived): CC0-1.0
Archived: 2026-10-17T06:33:58.758Z
SHA-256 of the archived text: dce1aa378bb5149a50927013d2786fc9dcb6b4d8d9c3038fa0e809f5f5a1cf52
Stable URL of this page: https://metraipolis.com/documento/dc_07bdf2935b8a

How to cite

Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment — arXiv:2609.01202 — arXiv (en), https://arxiv.org/abs/2609.01202 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_07bdf2935b8a (Metraipolis archive dc_07bdf2935b8a, sha256 dce1aa378bb5149a50927013d2786fc9dcb6b4d8d9c3038fa0e809f5f5a1cf52)

BibTeX

@misc{dc07bdf2935b8a,
  title = {Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment — arXiv:2609.01202},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01202},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_07bdf2935b8a; sha256 dce1aa378bb5149a50927013d2786fc9dcb6b4d8d9c3038fa0e809f5f5a1cf52},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01202. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY).
Pagina del lavoro: https://arxiv.org/abs/2609.01202 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T06:15:12.867Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).