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TRV-2026-0725Certified recordPeer-reviewed

AI-based augmentation of oncology clinical trials

Oncology clinical trials are often characterized by slow accrual, high failure rates and limited generalizability, reflecting both biological complexity and operational inefficiencies. Advances in artificial intelligence (AI) - enabled by large-scale electronic health record datasets and machine learning methods - offer new opportunities to address these challenges across the clinical trial lifecycle. In this Review, we discuss applications of AI across pre-trial design, trial conduct, and post-trial inference a…

Health · The Trace — both readings · certified 2026-08-10 · v1 · article view · machine-readable

Current reading — gain

AI augmentation of operational workflows under human oversight improves oncology trial feasibility and patient identification, with tools for enrollment screening and monitoring now implemented at select cancer centres.

Current reading — problem

AI augmentation of oncology trials faces cross-cutting equity, data quality and drift, transparency, and regulatory oversight challenges, while AI approaches intended to replace clinical evidence generation lack prospective validation.

What this doesn’t fix

Replacement uses such as synthetic control arms and digital twins have limited prospective validation and unresolved methodological and regulatory challenges, and broader adoption requires rigorous prospective validation and harmonized standards.

Evidence

Reader signal

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Truvace Impact Record TRV-2026-0725, v1: “AI-based augmentation of oncology clinical trials.” Truvace, 2026-08-10. /record/TRV-2026-0725 (accessed at citation time). sha256 c302da689889ee5b…

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