EMA & FDA: Ten Principles for Good AI Practice

In January 2026, EMA and the U.S. Food and Drug Administration (FDA) jointly identified ten principles for good artificial intelligence practice in the medicines lifecycle. The principles guide AI use in evidence generation and monitoring across all phases — from early research and clinical trials to manufacturing and safety monitoring. They apply to both human and veterinary medicines.

The ten principles

1. Human-centric by design — AI development and use align with ethical and human-centric values
2. Risk-based approach — proportionate validation, risk mitigation, and oversight based on context of use and model risk
3. Adherence to standards — compliance with legal, ethical, technical, scientific, cybersecurity, and regulatory standards including GxP
4. Clear context of use — a well-defined role and scope for why the AI is being used
5. Multidisciplinary expertise — appropriate expertise across relevant disciplines
6. Data governance and documentation — traceable, verifiable documentation of data provenance and processing in line with GxP
7. Model design and development practices — fit-for-use data, interpretability, explainability, and robust predictive performance
8. Risk-based performance assessment — evaluation of the complete system including human-AI interactions, with appropriate metrics and testing
9. Life cycle management — quality management, scheduled monitoring, and periodic re-evaluation to address data drift
10. Clear, essential information — plain-language information for users, patients, and regulators on context of use, performance, limitations, and updates

Why this matters internationally

These principles will underpin future AI guidance in different jurisdictions and support enhanced international collaboration among regulators, standards organisations, and stakeholders. EU guideline development is already underway, building on EMA's 2024 reflection paper.

Relevance for Hong Kong healthcare professionals

Hong Kong healthcare innovators and researchers working with pharmaceutical partners or multinational trials should expect these principles to shape global expectations for AI-generated clinical evidence. Even for day-to-day clinical AI tools, the themes — human oversight, risk proportionality, documentation, and lifecycle monitoring — translate directly into responsible professional practice.

Source: EMA/FDA — Guiding principles of good AI practice in drug development (January 2026)