P220: Formal Verification as Regulatory Validation for AI Across the Clinical Trial Lifecycle
Poster Presenter
Steven Thompson
Founder
Nextrial AI Brazil
Objectives
To propose and validate a three-gate regulatory verification architecture for AI systems across the clinical trial lifecycle that provides deterministic, auditable compliance evidence through formal mathematical proof, jurisdiction-specific regulatory verification, and mandatory human oversight.
Method
The framework was developed through analysis of regulatory requirements across FDA (21 CFR Parts 11, 50, 56, 312), ANVISA (Lei 14.874/2024, RDC 945), CDSCO (NDCTR 2019), and EU AI Act (2024/1689), then applied to an AI orchestration platform processing protocols across US, Brazil, and India.
Results
The three-gate architecture comprises: (1) CFM-1, a jurisdiction-specific regulatory verification engine using fine-tuned language model adapters (LoRA) trained on regulatory requirement sets for each target jurisdiction, with violations classified as Critical (blocks progression), Warning (flagged for review), or Info (logged); (2) Lean4 formal verification, generating mathematical proof certificates that verify structural properties of AI-generated regulatory artifacts including field presence, version consistency, reference resolution, and cross-document completeness, with each proof succeeding or failing deterministically in under 100 milliseconds; and (3) mandatory human oversight consistent with ICH E6(R2) Good Clinical Practice, with complete decision lineage captured in an immutable, 21 CFR Part 11-compliant audit trail.
In trial activation, the framework was applied to a Predictive Patient Eligibility Engine (PPEE) using physics-informed neural networks that model patient trajectories through differential equations rather than point-in-time snapshot matching. Against validated retrospective cohorts, PPEE achieved 87.3% eligibility prediction accuracy. Each prediction passes through all three verification gates before reaching a clinical coordinator. Proof certificates provide mathematically verifiable evidence that hard safety constraints were respected for each individual prediction.
In trial execution, the framework extends through protocol-aware plan-versus-actual tracking, enrollment velocity monitoring, and deviation detection. Protocol ingestion via structured JSON handoff enables the verification architecture to compare execution data against protocol-defined parameters, with variance thresholds triggering alerts through the same three-gate pipeline. The jurisdiction-specific adapter architecture enables simultaneous compliance verification across FDA, ANVISA, and CDSCO without separate validation systems.
Conclusion
AI systems in clinical trial operations require validation methodologies beyond statistical accuracy that provide deterministic, auditable compliance evidence. Current regulatory frameworks, including FDA's AI/ML Action Plan, the PCCP guidance, and the EU AI Act's conformity assessment, address AI systems producing diagnostic or therapeutic outputs but do not address AI orchestration systems that generate regulatory submissions, predict patient eligibility trajectories, monitor execution variance, or enforce compliance across multiple jurisdictions simultaneously.
The proposed three-gate framework addresses this gap. Formal verification proof certificates represent a fundamentally different class of evidence than statistical confidence intervals: mathematical proof that specific safety constraints were respected for each individual output, not a probability estimate across a population. This distinction has direct implications for regulatory submissions, audit readiness, and defensibility of AI-assisted clinical decisions.
The framework's lifecycle scope, from protocol ingestion through activation, execution monitoring, and feedback-driven refinement, demonstrates that a single verification architecture can govern AI systems across the trial workflow without requiring phase-specific validation methodologies.
Open questions remain regarding how proof certificates should map to the PCCP framework, the certification protocol for jurisdiction-specific regulatory adapters, and the role of patient preference data in defining bias boundaries for AI-assisted eligibility prediction. These questions require input from regulatory affairs, bioethics, and patient communities.
This framework is presented as a starting point for industry and regulatory community collaboration on validation standards for AI across the clinical trial lifecycle.
