FDA, with EMA · Life Science & Pharma: GxP - AI
Guiding Principles of Good AI Practice in Drug Development (joint FDA–EMA)
Final AI · Principles Last verified: not yet · Source last checked 3 Nov 2026
- Issued by
- FDA, with EMA
- Category
- AI
- Sub Category
- Principles
- Legal weight
- Guidance & Q&A
- Reuse
- Public domain
Ten high-level principles agreed by FDA and EMA for using AI well across a medicine's life. They are not binding.
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Lifecycle
Solid steps have happened. A dashed step is expected, and it is not a fact. Only dated steps in our record appear here.
- Published jointly by FDA and EMA (non-binding principles)
In plain English
Ten high-level, non-binding principles agreed by FDA (CDER, CBER) and EMA for using AI to generate evidence across the medicines life cycle, including manufacturing and quality. Not a regulation or formal guidance: intended to inform future guidance on both sides of the Atlantic.
Summary drafted with AI assistance. Not yet reviewed by the SME Reviewer. Read the official text before acting.
Context and history
FDA and EMA published these ten principles jointly in January 2026. They are high-level and non-binding, and they are meant to lay a foundation for good practice in using AI to generate evidence across the drug product life cycle, including manufacturing. They name areas for collaboration between regulators and standards bodies.
Who and what it applies to
Anyone using AI to generate or analyse evidence for drugs, across non-clinical, clinical, post-marketing and manufacturing phases.
What it contains
- 1 Human-centric by design
- Development and use align with ethical and human-centric values.
- 2 Risk-based approach
- Validation, risk mitigation and oversight are proportionate to the context of use and model risk.
- 3 Adherence to standards
- Legal, ethical, technical, scientific, cybersecurity and regulatory standards, including GxP.
- 4 Clear context of use
- A well-defined role and scope.
- 5 Multidisciplinary expertise
- Expertise in both the AI and its context of use, through the life cycle.
- 6 Data governance and documentation
- Data provenance, processing steps and analytical decisions are traceable and verifiable, in line with GxP, with privacy protected.
- 7 Model design and development practices
- Good design and software engineering, fit-for-use data, and attention to interpretability, explainability and predictive performance.
- 8 Risk-based performance assessment
- Evaluates the complete system, including human-AI interaction, with appropriate data and metrics.
- 9 Life cycle management
- Risk-based quality systems throughout, with scheduled monitoring and periodic re-evaluation, for example to address data drift.
- 10 Clear, essential information
- Plain-language information for users and patients on context of use, performance, limitations, data, updates and interpretability.
- Areas for collaboration
- Research, educational tools, international harmonisation and consensus standards.
Key points
- They are high-level and non-binding, and they are not guidance.
- They explicitly refer to GxP.
- Assessment covers the complete system, including human-AI interaction, not only the model.
- Monitoring and re-evaluation are expected, including for data drift.
- Plain-language information for users and patients is one of the ten.
In practice
General reading, not advice. This is drafted with AI and may not fit your situation. It does not replace the official text, your own quality system or qualified advice, and an inspector may take a different view.
- Use the ten principles as headings in an internal AI policy or gap assessment.
- Include the human step when you test an AI-assisted process.
- Plan periodic re-evaluation when a model goes live, not after a problem.
Read from the source document on 1 Nov 2026. This is our own description, not the official text. Check the full dated history in the change log below.
Change log
Key dated events only, not a complete history. Status after is the status once that event happened. Event kinds explained
| Date | Event | Status after | Source |
|---|---|---|---|
| 14 Jan 2026 | Published jointly by FDA and EMA (non-binding principles) | Final | Source for Published jointly by FDA and EMA (non-binding principles) |
Official source
The official text always prevails over anything on this site.
Source: FDA. Public domain.
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