Books and guidance on validating AI in GxP: a reading list

Search for a book on AI validation and you get machine learning textbooks. Cross-validation, holdout sets, model metrics: useful mathematics, and none of it answers the question a pharma quality professional is actually asking, which is how to validate an AI system so that a health-authority inspector accepts the evidence. The shelf for that question is short. This is what's on it, and the order I'd read it in.

Is there a book on validating AI in GxP?

Yes. Validating AI in GxP: A Practitioner's Guide (2026) is, as far as I know, the only book written specifically for pharma quality and validation professionals on this problem. I wrote it because I kept answering the same questions for quality leads who had regulatory expectations on one side, a data science team on the other, and nothing in between. It works from GAMP 5 and the draft Annex 22 down to the level a practitioner needs: risk tiers, worked examples, templates and SOPs, and the questions inspectors ask when they meet a validated model. Chapter 3 is free to read, no sign-up.

I'll declare the interest plainly: it's my book, and this is my site. So rather than review it, let me place it in the reading order and let Chapter 3 make the case itself.

What should you read before validating your first AI system?

Read in this order: GAMP 5 second edition for the framework and its AI appendix, the draft EU GMP Annex 22 for where European inspection expectations are heading, FDA's January 2025 draft guidance on AI for the credibility-assessment logic, and then a practitioner text that turns those expectations into working documents.

GAMP 5, second edition (ISPE, 2022) is the foundation, and its machine learning appendix was the first serious attempt to fit AI inside the established computerised-system lifecycle. If your team validates software today, this is the shared language. Read it before anything AI-specific, because every later document assumes it.

The draft EU GMP Annex 22 is the first dedicated GMP guideline for AI. Its consultation closed in October 2025 with around 1,300 comments, and a final version is expected toward the end of 2026. Draft or not, it tells you what European inspectors will ask: intended use, data lineage, the static-versus-adaptive distinction, human oversight, and monitoring. I've written a full reading of the draft separately.

FDA's draft guidance on AI in drug and biologic development (January 2025) matters even if you never file with the FDA, because its risk-based credibility framework, define the question of interest, then size the evidence to the model's influence and the decision's consequence, is the cleanest statement of the logic that all AI validation rests on. Alongside it, the final CSA guidance (February 2026) sets the wider risk-based assurance context that AI validation lives inside.

For the mathematics underneath, the general machine learning literature on model evaluation is worth a detour if you have data scientists to manage; the NCBI Bookshelf chapter on AI model development and validation in healthcare is a solid free starting point. Just don't mistake model evaluation for validation. One tells you the model performs; the other proves to a regulator that the system is fit for its intended use and stays that way.

Do GAMP 5 and Annex 22 tell you how to validate AI?

They set the expectations, and they do it well: risk-based thinking, lifecycle control, data quality, human oversight. What they leave to you is the how: the actual risk tiers, acceptance criteria, test design against drift, and the evidence pack an inspector reads. That gap between expectation and working document is where most teams stall.

Closing that gap is what a practitioner's book is for, and it's the standard I held mine to: a quality lead should be able to take a template from an appendix and use it that week. The 28 templates exist because "be risk-based" is advice, and a filled-in risk tiering with a signature line is a document. Guidance can't give you the second one. Someone who has written it under inspection pressure can.

The shelf will grow. Annex 22 finalising toward the end of 2026 will bring a wave of interpretive writing, and the field needs more worked case studies than any one author has. For now, the honest summary is: two regulatory documents, one FDA framework, one practitioner's book, and Chapter 3 of that book is free, so the reading list costs you nothing to start.

Sachin Bhandari is the author of Validating AI in GxP: A Practitioner's Guide and advises pharma, biotech and CDMO organisations on AI in GxP, CSV to CSA and digital validation. The AI Governance Stack and the other frameworks behind the book are on the frameworks page, free to download. A live practitioner course on validating AI in GxP is coming soon.