At the end of 2024, the European Medicine’s agency (EMA) published a reflection paper on ‘The use of Artificial Intelligence (AI) in the medicinal product lifecycle,’ [1]. This was based on the draft guidance issued in 2023 [2].
In the final guidance, the EMA uses the definition of AI systems developed by the Organisation for Economic Co-operation and Development (OECD), which states that an AI system is “a machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments”[1].
In the guidance, the EMA emphasizes a risk-based and human-centered approach to the use of AI across all stages of the medicinal product lifecycle, from drug discovery to post-authorization activities.
1. Risk-Based and Human-Centered Regulation
- EMA expects AI regulation to be grounded in continuous risk assessment, with safeguards tailored to the AI’s autonomy level and the specific use case.
- Higher-risk applications (e.g., precision medicine) require more robust controls than lower-risk uses (e.g., early-stage data analysis).
- This is given in a two-pronged approach, focusing on patient safety and impact on regulatory decisions. For example, the categories ‘high patient risk’ and ‘high regulatory impact’, are included to prevent confusion with the definition of high-risk AI under the EU AI Act.
- A human-centric approach is essential, especially during data collection and modelling, addressing concerns such as algorithmic bias and the use of non-transparent (black box) models.
2. Guidance Highlights
- The guidance provides lifecycle-based risk mitigation strategies, addressing technical elements like data quality, model training/testing, explainability, and governance.
- EMA aligns AI practices with existing frameworks (e.g., GxP, ICH Q8/Q9/Q10) while promoting traceability and compliance.
- While transparency in AI is key, in some low-risk circumstances black-box models may be acceptable if transparent ones underperform.
- Emphasis that the market authorisation applicant, marketing authorisation holder and the (named) clinical trial sponsor and manufacturer are responsible parties. At all stages of development and marketing these stakeholders can make use of AI and their responsibilities are underlined.
In terms of outlook, the EMA currently positions itself as regulatory yet innovation-friendly, differentiating from more conservative data protection regulators. The EMA emphasizes the need for a balanced collaborative approach prioritizing patient safety and regulatory integrity while facilitating the transformative potential of AI. It is key to note that the reflection paper considers the AI Act [3] during finalization to ensure that the EMA’s approach aligns with the broader regulatory framework governing AI.
The US Food and Drug Administration issued draft guidance on the use of AI in pharmaceutical regulation in January 2025 [4].
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References
1. The use of Artificial Intelligence (AI) in the medicinal product lifecycle | European Medicines Agency (EMA
2. https://www.ema.europa.eu/en/documents/scientific-guideline/draft-reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf
3. Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle’, EMA (9 September 2024), EMA/CHMP/CVMP/83833/2023, p. 3.
4. GaBI Online - Generics and Biosimilars Initiative. FDA issues guidance on AI use in drug and biologicals regulatory decision making [www.gabionline.net]. Mol, Belgium: Pro Pharma Communications International; [cited 2025 Jun 20]. Available from: www.gabionline.net/guidelines/fda-issues-guidance-on-ai-use-in-drug-and-biologicals-regulatory-decision-making
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