PFIZER - Trust but Verify: A Practical Statistical Framework for Evaluating AI-Generated Regulatory Content

Session details:

Generative AI can draft regulatory documents in a fraction of the usual time, but a fluent draft is not automatically correct. AI outputs vary from run to run and can be confidently wrong, and teams can't responsibly scale them for regulatory use without a reliable way to prove accuracy.

This session shares a practical evaluation framework built to close that gap. It pairs automated, data-driven checks, grounding to source data, completeness, factual accuracy, and consistency across repeated runs, with structured review by medical, regulatory, and quality experts. Crucially, the depth of evaluation scales to each document's risk and context of use, so the highest-stakes content gets the most scrutiny.

Key takeaways:

• How to judge whether an AI-generated document is accurate and ready for regulatory use

• How to combine automated checks with expert human review to build trust in AI outputs

• Why traditional evaluation methods fall short for AI, and what to use instead