The use of AI technologies across the medicine lifecycle has increased significantly in recent years. AI holds great promise as a tool to accelerate the path from innovation to safe and effective medicines.
Principle #1 The development and use of AI technologies align with ethical and human-centric values.
The European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) have agreed to ten principles for the use of artificial intelligence (AI) practice in the development of medicines and subsequent use (the ‘lifecycle’.)
Principle #2 The development and use of AI technologies follow a risk-based approach with proportionate validation, risk mitigation, and oversight based on the context of use and determined model risk.
The ten principles are designed to provide broad guidance on AI use in evidence generation and monitoring across all phases of a medicine, from early research and clinical trials to manufacturing and safety monitoring.
Principle #3 AI technologies adhere to relevant legal, ethical, technical, scientific, cybersecurity, and regulatory standards, including Good Practices (GxP).
It is expected that these principles will underpin future AI guidance in the different jurisdictions and support enhanced international collaboration among regulators, organisations setting technical standards and other stakeholders.
Principle #4 AI technologies have a well-defined context of use (role and scope for why it is being used).
The work builds on the EMA AI reflection paper published in 2024 and this provides, in the current international context, a rare example of European and U.S. cooperation.
Principle #5 Multidisciplinary expertise covering both the AI technology and its context of use are integrated throughout the technology’s life cycle.
According to European Commissioner for Health and Animal Welfare, Olivér Várhelyi: “The guiding principles of good AI practice in drug development are a first step of a renewed EU-US cooperation in the field of novel medical technologies. The principles are a good showcase of how we can work together on the two sides of the Atlantic to preserve our leading role in the global innovation race, while ensuring the highest level of patient safety.”
Principle #6 Appropriate governance, including privacy and protection for sensitive data, is maintained throughout the technology’s life cycle.
The new pharmaceutical legislation accommodates the broader use of AI in the lifecycle of medicines in regulatory decision-making, and creates additional possibilities for testing innovative AI driven methods for medicines in a controlled environment.
Principle 7 Good model and system development promotes transparency, reliability, generalisability, and robustness for AI technologies contributing to patient safety.
The two regulators agree that for the plan to work, AI needs to be expertly managed, including the mitigation of risks. As AI continues to evolve, a principles-based approach will help regulators, pharmaceutical companies and medicines developers harness the potential of these technologies while ensuring patient and animal safety and regulatory compliance.
Principle #8 Risk-based performance assessments evaluate the complete system including human-AI interactions, using fit-for-use data and metrics.
It is anticipated that the principles will eventually be supplemented by additional guidance taking into account the applicable legal requirements and relevant new legislation in the area of medicines.
Principle #9 The AI technologies undergo scheduled monitoring and periodic re-evaluation to ensure adequate performance (e.g., to address data drift).
The changes align with the EMA’s mission to promote the safe and responsible use of AI, as outlined in the European medicines agencies network strategy (EMANS) to 2028 with its goal of leveraging data, digitalisation and AI.
Principle #10 Clear, essential information. Plain language is used to present clear, accessible, and contextually relevant information to the intended audience, including users and patients, regarding the AI technology’s context of use, performance, limitations, underlying data, updates, and interpretability or explainability.
