EMA researchers map AI research priorities across the medicines lifecycle

EMA researchers map AI research priorities across medicines lifecycle

Key Takeaways

• Accuracy and reliability of AI tools ranked as the highest research priority, according to EMA-led findings on regulatory research priorities for AI.
• The EMA has extended its AI-enabled Scientific Explorer tool to help regulators and national competent authorities retrieve information related to initial marketing authorisation applications for human medicines.
• The Data and AI in medicines regulation to 2028 plan sets a five-year framework for how European regulators will manage, analyze, and share health data to support research, improve interoperability, and enable more timely authorization of medicines.
• Future regulatory affairs trends point toward big data, AI, and machine learning in regulatory processes to enable real-time regulation.

IntelligenceRegulatory Impact▾

EMA decisions frame this story. Regulatory relevance is medium for this topic. Track designations, submission types, and label or guidance shifts that could move timelines.

EMA and collaborators identified AI research priorities across the medicines lifecycle

The European Medicines Agency and its collaborators ran a survey to identify priority research areas for artificial intelligence across the medicines lifecycle. According to the research published in the RAPS journal of regulatory affairs, accuracy and reliability of AI tools emerged as the highest priority across all groups by a substantial margin.

The survey covered research needs spanning product development through post-authorization monitoring and evaluation. The identification of these priorities signals where the EMA and its partners see the greatest need for evidence and methodological development as AI applications expand across regulatory processes.

IntelligenceCompetitive Intelligence▾

Competitive pressure is high. the parties involved reshape positioning, formulary leverage, and partnership options. Benchmark pipeline differentiation and regional market access assumptions against this development.

EMA's Scientific Explorer shows current AI use in regulatory work

The EMA has already deployed AI tools in its regulatory operations. The agency extended the functionality of its AI-enabled knowledge mining tool, Scientific Explorer, to support EMA and national competent authorities in finding information related to initial marketing authorisation applications for human medicines.

This tool represents the EMA's current operational use of AI in regulatory work, even as the agency continues to research best practices for accuracy and reliability across the broader regulatory lifecycle.

IntelligenceMarket Signals▾

Commercial pull is medium and investment relevance medium for this topic. Expect implications for pricing, access, and launch sequencing.

The 2028 data and AI plan defines the regulator's longer-term framework

The EMA and Heads of Medicines Agencies (HMA) have jointly published a five-year plan titled Data and AI in medicines regulation to 2028. The plan outlines how European regulators will manage, analyze, and share regulatory and health data to support research, improve interoperability, and enable more timely authorization of medicines.

IntelligenceStrategic Takeaways▾

Accuracy and reliability of AI tools ranked as the highest research priority, according to EMA-led findings on regulatory research priorities for AI . The EMA has extended its AI-enabled Scientific Explorer tool to help regulators and national competent authorities retrieve information related to initial marketing authorisation applications for human medicines. The Data and AI in medicines regulation to 2028 plan set

Future regulatory affairs trends point toward big data, AI, and machine learning

Broader trends in the regulatory affairs profession increasingly emphasize advanced technologies in core regulatory processes. Research on future directions in regulatory affairs identifies big data, artificial intelligence, and machine learning as key trends in regulatory processes, with a particular focus on enabling real-time regulation.

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