Guiding Principles for Good AI Practice in Drug Development (FDA & EMA)
Extends AI governance expectations beyond devices into the medicinal product lifecycle.
Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle
EMA’s core position on AI use in clinical development, pharmacovigilance, manufacturing and regulatory submissions.
EMA–HMA Network Data Steering Group Workplan (2025–2028)
Outlines EMA’s forward-looking AI and data strategy, including experimentation cycles and regulatory science development.
https://www.ema.europa.eu/en/documents/other/network-data-steering-group-workplan-2025-2028_en.pdf
Guiding Principles for Good AI Practice in Drug Development (Joint EMA–FDA)
Signals transatlantic alignment on AI governance in medicines development.
https://www.ema.europa.eu/en/news/ema-fda-set-common-principles-ai-medicine-development-0
EU Artificial Intelligence Act (Regulation (EU) 2024/1689)
Classifies many medical AI systems as “high-risk” and introduces explicit requirements for data governance, logging, human oversight and lifecycle control that sit alongside MDR/IVDR.
https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Software and Artificial Intelligence (AI) as a Medical Device
Explains how AI is currently regulated under UK medical device law.
Software and AI as a Medical Device Change Programme: Roadmap
Outlines planned UK reforms addressing adaptivity, transparency and AI-specific regulatory challenges.
Machine Learning Medical Devices: Transparency Principles
Defines expectations for clear communication of AI system limitations and performance.
https://www.gov.uk/government/publications/machine-learning-medical-devices-transparency-principles
Predetermined Change Control Plans for Machine Learning-Enabled Medical Devices: Guiding Principles
Reinforces lifecycle governance expectations for adaptive systems.
Impact of AI on the Regulation of Medical Products
MHRA’s broader reflection on regulatory implications of AI across healthcare products.
https://www.gov.uk/government/publications/impact-of-ai-on-the-regulation-of-medical-products
Draft Annex 22 – Artificial Intelligence (GMP Guide)
Introduces GMP-specific requirements for AI selection, validation, monitoring, change control and human oversight.
Revised Annex 11 – Computerised Systems (Draft Update)
Strengthens lifecycle management and data integrity expectations for computerised systems, including AI-enabled systems.
Revised Chapter 4 – Documentation (Draft Update)
Why it matters: Reinforces documentation and data governance requirements relevant to AI-enabled workflows.
Artificial Intelligence and Machine Learning (AI/ML) in Software as a Medical Device (SaMD)
Central index for FDA’s approach to AI/ML-enabled medical devices, including discussion papers, action plans and lifecycle expectations.
Proposed Regulatory Framework for Modifications to AI/ML-Based SaMD (2019 Discussion Paper)
Introduced the concept of a “Predetermined Change Control Plan” (PCCP) for adaptive AI systems. This is foundational to FDA’s lifecycle thinking.
AI/ML-Based SaMD Action Plan (2021)
Sets out FDA’s strategic direction for AI oversight, including GMLP, transparency, real-world performance monitoring and regulatory science.
https://www.fda.gov/media/145022/download
Marketing Submission Recommendations for a Predetermined Change Control Plan (PCCP) for AI-Enabled Device Software Functions
Provides specific expectations on how manufacturers can manage post-market learning and updates in AI systems.
Good Machine Learning Practice (GMLP) for Medical Device Development: Guiding Principles
Defines baseline expectations for dataset representativeness, separation of training/testing, performance evaluation and lifecycle control.
Transparency for Machine Learning-Enabled Medical Devices: Guiding Principles
Clarifies expectations around communicating AI limitations, intended use and performance to users.
