Do the FDA and EMA's Good AI Practice principles apply to AI used in regulatory writing?

Not directly. The joint principles the FDA and the European Medicines Agency (EMA) published in January 2026 are about AI used to generate or analyze evidence across a drug's life cycle. They don't mention drafting or reviewing labeling text. Teams using AI for writing may still find them a useful frame, but that's our reading, not something the document says.

This page is general information, not legal or regulatory advice.

What the document covers

The document, Guiding Principles of Good AI Practice in Drug Development (January 2026), defines its scope this way:

In this context, AI refers to system-level technologies used to generate or analyze evidence across the drug product life cycle, including nonclinical, clinical, post-marketing, and manufacturing phases.

It describes the principles as intended to lay the foundation for developing good practice, and as identifying areas where regulators, standards organizations and others could work together. Our reading: it isn't framed as a binding rule.

The principles, in the document's words

From Guiding Principles of Good AI Practice in Drug Development (FDA and EMA, January 2026):

Human-centric by design The development and use of AI technologies align with ethical and human-centric values.

Risk-based approach The development and use of AI technologies follow a risk-based approach with proportionate validation, risk mitigation, and oversight based on the context of use and determined model risk.

Adherence to standards AI technologies adhere to relevant legal, ethical, technical, scientific, cybersecurity, and regulatory standards, including Good Practices (GxP).

Clear context of use AI technologies have a well-defined context of use (role and scope for why it is being used).

Multidisciplinary expertise Multidisciplinary expertise covering both the AI technology and its context of use are integrated throughout the technology's life cycle.

Data governance and documentation Data source provenance, processing steps, and analytical decisions are documented in a detailed, traceable, and verifiable manner, in line with GxP requirements. Appropriate governance, including privacy and protection for sensitive data, is maintained throughout the technology's life cycle.

Model design and development practices The development of AI technologies follows best practices in model and system design and software engineering and leverages data that is fit-for-use, considering interpretability, explainability, and predictive performance. Good model and system development promotes transparency, reliability, generalizability, and robustness for AI technologies contributing to patient safety.

Risk-based performance assessment Risk-based performance assessments evaluate the complete system including human-AI interactions, using fit-for-use data and metrics appropriate for the intended context of use, supported by validation of predictive performance through appropriately designed testing and evaluation methods.

Life cycle management Risk-based quality management systems are implemented throughout the AI technologies' life cycles, including to support capturing, assessing, and addressing issues. The AI technologies undergo scheduled monitoring and periodic re-evaluation to ensure adequate performance (e.g., to address data drift).

Clear, essential information Plain language is used to present clear, accessible, and contextually relevant information to the intended audience, including users and patients, regarding the AI technology's context of use, performance, limitations, underlying data, updates, and interpretability or explainability.

What they don't say

The principles don't mention labeling, product information or writing tools, and they don't set a specific human-review step for AI-drafted text. For what the FDA's January 2025 draft guidance on AI does and doesn't cover, see is there FDA guidance on AI for drug labeling. For EMA's statement on AI and product information, see who is responsible for AI-drafted label errors. For how the FDA itself uses AI, see does the FDA use AI to review drug labels.

Is the FDA's AI credibility guidance final?

No, as of October 14, 2026. The FDA's guidance page still lists Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products as a draft guidance dated January 2025. We'll update this page at the same address if that changes.

What a writing team can take from them

Our view, not the document's: the clear-context-of-use principle's idea of a clear context of use translates well to writing tools. Write down what the tool is used for, which text it touches, and who checks the result for meaning. Keep a record of AI-assisted steps under your quality system. Our AI second-reader guide covers the practical side.

Sources

Last reviewed: October 14, 2026

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FAQ

What are the FDA–EMA Good AI Practice principles?

A joint set of principles published by the FDA and EMA in January 2026 for AI used to generate or analyze evidence across a drug's life cycle.

Do they apply to AI writing tools?

Not directly. The document's scope is AI used to generate or analyze evidence; it doesn't mention labeling or writing tools. Applying it to writing is our reading.

Is the FDA's AI credibility guidance final?

Not as of October 14, 2026. The FDA's guidance page still lists it as a draft guidance from January 2025.

What does context of use mean for a writing tool?

In our view: saying exactly what the tool is used for, which text it touches, and who checks the result.