Frank Fagan (South Texas College Law Houston) has posted “The Governance of Clinical Trial Data” (Washington Journal of Law & Policy, forthcoming 2026) on SSRN. Here is the abstract:
Clinical trial results are among the most authoritative inputs in biomedical research and are increasingly reused as training, calibration, and evaluation data for AI systems. Yet a large share of completed trials, especially small academic studies, pilot projects, and early-phase experiments, never disclose their findings. These omissions distort meta-analyses, hide negative results, and silently propagate upstream bias into AI models. Existing enforcement tools, including FDA civil penalties and NIH grant sanctions, work tolerably well for high-stakes commercial trials when used, but fail across the long tail, where investigators lack the resources or incentives to upload results once grant funds expire.
This Article reframes clinical-trial transparency as a training-data governance problem and proposes a complementary solution: modest fixed cost-offsets for compliant uploads, funded through a two-tier access model. Public summaries on ClinicalTrials.gov would remain free, while a licensed API would provide harmonized, machine-readable datasets to high-volume users such as insurers, pharmaceutical consortia, and AI developers. Aligning the distribution of costs and benefits makes transparency feasible for resource-constrained investigators and sustainable for the analytic institutions that depend on complete, unbiased data. The result is a light-touch institutional architecture that preserves fragile trial evidence and strengthens the reliability of both biomedical research and AI health systems.