The rush to deploy AI for regulatory writing has outpaced most sponsors' ability to validate these tools properly. Recent FDA and EMA guidance, plus the ISPE GAMP AI Guide, now give sponsors a clearer compliance framework, but the burden remains on your team to distinguish platforms that can withstand regulatory scrutiny from those that create risk.
Start with validation architecture. If a vendor cannot produce IQ/OQ/PQ documentation covering the AI model itself, or lacks a documented change control process for model updates, that is a material gap. Next, address hallucination risk directly. Platforms grounded strictly in your source documents using retrieval-augmented generation carry far lower risk than those drawing on general training data. Ask whether the system flags low-confidence outputs for human review rather than presenting all generated text as equally reliable.
Cross-document consistency is a third critical area. Protocol amendments, endpoint definitions, and dosing regimens must align across protocols, informed consent forms, and clinical study reports. Manual tracking of these elements is a common source of regulatory queries. Platforms that track shared elements across documents and flag inconsistencies offer measurable risk reduction.
Finally, confirm 21 CFR Part 11 compliance for audit trails, access controls, and time-stamped entries. AI-generated documents submitted to FDA fall squarely within that scope.