Abstract
Clinical trials in oncology represent a moral and scientific contract: Patients accept risk and uncertainty with the expectation that results will be reported fully, accurately, and transparently to inform future care. Despite regulatory mandates and journal policies, nonpublication, selective outcome reporting, weak linkage between protocols, registries, and manuscripts, and limited access to individual participant data remain common, particularly among investigator-initiated trials. In an era of artificial intelligence (AI)-driven evidence synthesis and precision oncology, opaque or selectively curated trial data threaten the validity of clinical evidence and risk propagating bias into downstream AI systems. This commentary argues that data transparency must be treated as foundational infrastructure for AI-ready oncology trials rather than as a discretionary compliance task. We describe how AI can function as a “transparency engine” by automating trial–publication linkage, detecting protocol-to-article discordance, and converting unstructured trial reports into standardized, machine-readable results. Embedded within journal workflows and applied with appropriate human oversight, these tools can shift transparency from episodic auditing to continuous, verifiable practice—honoring obligations to trial participants and strengthening the reliability of oncology evidence and AI-enabled care. This commentary is intended for trialists, journal editors, regulators, funders, and developers of AI systems in precision oncology.
Get full access to this article
View all access options for this article.