Artificial intelligence is increasingly being applied across the medicine development lifecycle, including early research, clinical trials, manufacturing, and safety monitoring. As the use of AI expands, regulatory clarity has become essential. On January 14, 2026, the European Medicines Agency and the U.S. Food and Drug Administration jointly published a set of common principles to guide the responsible use of AI in medicine development.
These principles reflect a shared regulatory view on how AI systems should be developed, validated, implemented, and monitored when used to support regulatory decision making. The initiative highlights growing collaboration between global regulators to promote scientific consistency while enabling innovation.
AI based tools are now being used in areas such as clinical trial design, patient selection, medical imaging analysis, safety signal detection, and real world data evaluation. While these applications offer efficiency and analytical benefits, they also raise important considerations related to data quality, transparency, reproducibility, and accountability.
The EMA FDA principles aim to address these challenges by outlining high level expectations that apply across different stages of drug development. The objective is to ensure that AI generated outputs used in regulatory submissions are reliable, scientifically valid, and appropriate for their intended use.
This collaboration builds on earlier regulatory initiatives, including EMA’s 2024 reflection paper on AI and ongoing efforts to integrate digital and data driven technologies into regulatory frameworks. The shared principles are expected to inform future guidance in both regions and support regulatory alignment between Europe and the United States.
Key Principles of Good AI Practice
The ten guiding principles published by the agencies describe fundamental expectations for AI applications in drug development:
- Human-centric design – AI tools should support human judgement and ethical values.
- Risk-based approach – Development, validation and use of AI should be proportional to the risks posed in its specific context.
- Adherence to standards – AI systems should comply with applicable technical, scientific and regulatory standards.
- Clear context of use – Sponsors must define where and how AI will be used within the development process.
- Multidisciplinary expertise – Teams should include relevant domain experts to oversee and validate AI tools.
- Data governance and documentation – Strong data quality, traceability and documentation practices are essential.
- Model design and development practices – Models should be developed and reported transparently.
- Risk-based performance assessment – Verification and validation should be tailored to the intended use.
- Life cycle management – Sponsors must monitor AI performance over time and manage updates responsibly.
- Clear, essential information – Outputs of AI systems should be communicated clearly and accessibly to reviewers and users.
Industry Implications
For pharmaceutical companies, biotech organizations, and contract research organizations, these principles provide early insight into how regulators are likely to assess AI enabled approaches during submissions and inspections.
While the document does not introduce new regulatory requirements, it establishes a common foundation for future AI specific guidance in both the United States and Europe. Organizations using AI in clinical trials, data analysis, or regulatory support activities should consider aligning their internal processes with these principles.
This includes maintaining clear documentation of AI tools used in studies, implementing defined governance structures, ensuring multidisciplinary oversight, and demonstrating that AI outputs are fit for their intended purpose. Early engagement with regulators may become increasingly important where AI plays a significant role in development or decision making.
Looking Ahead
The joint EMA FDA principles reinforce the importance of international alignment in the regulation of AI and digital technologies in medicine development. As regulatory expectations continue to mature, organizations that proactively align with these principles will be better positioned to support efficient global development programs and regulatory interactions.
About Lambda: Lambda Therapeutic Research is a leading full-service Global Clinical Research Organization (CRO) headquartered in Ahmedabad (India), with facilities and operations in Mehsana (India), Las Vegas (USA), Toronto (Canada), London (UK), Barcelona (Spain), and Warsaw (Poland). Lambda provides comprehensive end-to-end clinical research services to the global innovator, biotech, and generic pharmaceutical industries.
