What question did this study set out to answer?

The aim is to establish an implementation framework for integrating AI proof certificates within existing clinical data standards.

May 19, 2026Open Access

Anchoring AI Proof Certificates to Clinical Data Standards: A USDM-Based Implementation Framework for Regulatory Interoperability in Clinical Trials

Key Points

The aim is to establish an implementation framework for integrating AI proof certificates within existing clinical data standards.
Proposed a three-gate schema for regulatory compliance verification, structural verification, and human oversight.
Specified field-level requirements, conformance rules, controlled vocabularies, and programmatic dependencies.
Addressed multi-jurisdictional interoperability and audit trail architecture.
Introduced specifications for regulatory compliance that align with the CDISC USDM.
Outlined governance structures for continuous learning and biomedical concept verification.
Included a data dictionary to support implementation.

Abstract

This working paper proposes a field-level implementation framework for anchoring AI proof certificates natively within the CDISC Unified Study Definitions Model (USDM) without requiring new standards or infrastructure. The framework specifies a three-gate schema for regulatory compliance verification, formal structural verification, and human oversight attestation, including field-level specifications, conformance rules, controlled vocabularies, and programmatic dependencies. Additional topics include PCCP integration, continuous learning governance, biomedical concept verification via the BiomedicalConceptSurrogate trap, multi-jurisdictional interoperability, and audit trail architecture. Companion data dictionary included. Version 1.0 Working Draft — explanatory narrative sections in progress. Builds upon Thompson, S. (2026). Toward a Regulatory Validation Framework for AI-Assisted Clinical Trial Activation and Execution. NexTrial Dispatch, March 2026.

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