EMA's AI Strategy for Medicines Regulation

The European medicines regulatory network aims to enable regulatory systems in the European Union to use the capabilities of artificial intelligence (AI) while managing its risks. Capabilities include personal productivity, process automation, better insights into data, and decision-making support — all for the benefit of public and animal health.

AI is key to leveraging large volumes of regulatory and health data. This encourages research and innovation and supports regulatory decision-making for safe, effective, and high-quality medicines that reach patients faster.

The 2025–2028 workplan

The Network Data Steering Group's workplan for 2025–2028 identifies actions in four key AI-related areas:

1. Guidance, policy and product support — delivering guidance on the use of AI throughout the medicine lifecycle
2. Tools and technology — providing frameworks for the use of AI tools
3. Collaboration and change management — developing capacity for AI technology and preparing regulators for the AI transformation
4. Experimentation — ensuring a structured and coordinated approach

This workplan integrates and expands on the AI workplan set up by the former Big Data Steering Group (2023–2028).

Reflection paper on AI in the lifecycle

A reflection paper on the use of AI in the medicinal product lifecycle provides considerations to help medicine developers and marketing authorisation applicants use AI and machine learning safely and effectively at different lifecycle stages. CHMP and CVMP adopted the paper in September 2024.

Developers should understand these considerations alongside EU legal requirements on AI, data protection, and medicines regulation.

Relevance for Hong Kong healthcare professionals

While EMA regulates medicines in the EU, its strategic framing mirrors challenges familiar in Hong Kong: rising data volumes, pressure to work more efficiently, and the need to adopt AI without compromising patient safety or professional accountability. Hong Kong clinicians, pharmacists, and administrators can use this four-pillar model — guidance, tools, capacity-building, and structured experimentation — when shaping local AI literacy programmes or hospital governance frameworks.

Sources: EMA — Artificial intelligence; Reflection paper on AI in the medicinal product lifecycle