Clinical AI Validation Consulting Under GCP and GAMP 5

A clinical AI system, one triaging adverse event reports, screening candidates for trial eligibility, or drafting sections of a clinical study report, sits inside a trial governed by GCP and runs on a computerized system governed by GAMP 5. GCP asks whether the trial's quality management is proportionate to risk to participants and data integrity. GAMP 5 asks whether the system itself was validated for its intended use. An AI model that produces different output on repeated runs strains GAMP 5's traditional deterministic test-script approach, which is exactly why ISPE expanded GAMP 5's appendices for AI/ML and published a standalone AI guide.

Neither standard exempts AI from its requirements. A trial team cannot point to E6(R3)'s new flexibility as a reason to skip system validation, and cannot point to GAMP 5's system-level validation as a reason to skip GCP's participant-risk oversight. Both apply, together, to the same AI system.

The work starts with an inventory of every AI or ML system touching trial data, patient interaction, or safety reporting, each mapped to its GCP risk category and its GAMP 5 system classification.

Validation follows GAMP 5's risk-based method, adapted for non-deterministic AI output per its expanded AI/ML appendix coverage: structured output sampling against a scoring rubric, documented review of a representative output sample, and ongoing drift monitoring, all captured in an audit trail that satisfies E6(R3)'s record and oversight expectations.

The deliverable is a validation package built to satisfy an inspector working from either standard: the E6(R3) risk-based quality management rationale, the GAMP 5 validation protocol and supporting evidence, and the ongoing monitoring plan, not a one-time slide deck.