Artificial intelligence is no longer a niche regulatory topic in pharmaceutical development.
At RAPS Convergence 2026, held in Charlotte in September, representatives from FDA, EMA and industry discussed how rapidly AI use is expanding across the medicines lifecycle and why international harmonisation is becoming increasingly important.
One number is particularly striking.
According to Gabriel Innes, Assistant Director for Data Science and AI Policy at FDA’s Center for Drug Evaluation and Research (CDER), the Center has now received more than 1,500 regulatory submissions involving AI.
These submissions include:
- Investigational New Drug applications (INDs);
- New Drug Applications (NDAs);
- Biologics License Applications (BLAs);
- Abbreviated New Drug Applications (ANDAs);
- Emergency Use Authorisations;
- Drug Development Tools.
The implication is important:
AI is moving from experimental innovation toward routine regulatory practice.
The challenge for regulators is therefore changing.
The question is increasingly not whether AI will be used in pharmaceutical development, but how regulators can establish sufficiently consistent expectations for its use.
More Than 1,500 AI-Related Submissions Changes the Regulatory Context
FDA had previously reported experience with more than 500 submissions containing AI components between 2016 and 2023.
The figure reported at RAPS Convergence indicates how rapidly this activity has expanded.
This matters because regulatory frameworks designed when AI use was relatively uncommon now need to support much larger numbers of applications.
For pharmaceutical companies, this also means that regulatory experience with AI is accumulating quickly.
AI can no longer be treated solely as:
an innovation project → a pilot → a future regulatory problem.
It increasingly needs to be treated as part of normal pharmaceutical development and regulatory strategy.
Context of Use Remains Central
FDA’s current approach is based strongly on Context of Use.
Its January 2025 draft guidance on AI supporting regulatory decision-making proposes a risk-based credibility framework in which the necessary level of assurance depends on:
- what the AI model does;
- how its output is used;
- how important that output is to the regulatory decision;
- what could happen if the output is wrong.
This creates a fundamentally different question from:
“Is this AI model validated?”
The more useful question becomes:
“Is this AI model sufficiently credible for this particular Context of Use?”
A model used to identify information for expert review does not necessarily require the same evidence as a model generating information that materially influences a safety, efficacy or quality conclusion.
FDA and EMA Already Agree on the High-Level Principles
In January 2026, FDA and EMA jointly published ten Guiding Principles of Good AI Practice in Drug Development.
The principles cover:
- human-centric design;
- risk-based approaches;
- adherence to relevant standards and GxP;
- clear Context of Use;
- multidisciplinary expertise;
- data governance and documentation;
- model design and development practices;
- risk-based performance assessment;
- lifecycle management;
- clear communication of essential information.
Importantly, these principles apply across the pharmaceutical lifecycle, including:
nonclinical development → clinical development → manufacturing → post-marketing activities.
This means that international regulatory convergence has already started at the level of general principles.
But Principles Are Not the Same as Standards
The discussion at RAPS Convergence highlighted an important gap.
Regulators can agree on principles such as:
reliability → transparency → lifecycle management → human oversight
while companies can still face uncertainty about how to demonstrate compliance in practice.
Examples include:
- Which performance metrics should be used?
- How much independent test data are necessary?
- What constitutes sufficient robustness?
- How should uncertainty be documented?
- When does a model change require revalidation?
- How should third-party AI models be controlled?
- What evidence is needed for foundation models or externally hosted AI?
This is where international technical standards and more detailed regulatory guidance become important.
EMA: Accuracy, Robustness and Explainability Remain Priority Questions
During the RAPS discussion, EMA highlighted findings from its recent regulatory science work.
Three questions remain particularly important:
- How can AI accuracy and reliability be demonstrated?
- How can models remain robust and trustworthy as data and conditions change?
- When is explainability important for regulatory decision-making?
These questions point toward a lifecycle concept of AI assurance.
Initial validation may demonstrate acceptable performance at deployment.
But regulated pharmaceutical use may also require evidence that the model continues to perform adequately over time.
This introduces:
performance monitoring → drift detection → change assessment → reassessment / revalidation.
International Convergence Is Already Broader Than FDA and EMA
EMA also described collaboration with organisations including the International Coalition of Medicines Regulatory Authorities (ICMRA) and the World Health Organization.
This is important for multinational pharmaceutical companies.
AI systems are rarely developed for one regulatory jurisdiction only.
A global pharmaceutical company may use the same AI platform across:
- the United States;
- the European Union;
- Canada;
- Japan;
- other PIC/S or ICH jurisdictions.
Substantially different national AI-validation expectations would create considerable complexity.
International convergence therefore has practical value beyond regulatory policy.
It could eventually allow companies to develop a more consistent global evidence package for AI systems.
Human Decision-Making Remains Central
Another important point concerned the regulator’s own use of AI.
FDA is itself adopting AI technologies internally.
However, the model described by FDA remains augmentation rather than replacement of expert regulatory judgment.
This principle has obvious relevance for pharmaceutical companies.
AI can support:
- information retrieval;
- data analysis;
- document review;
- regulatory intelligence;
- signal identification;
- drafting;
- workflow automation.
But the existence of an AI output does not transfer accountability away from the person responsible for the regulated decision.
This principle is especially important when AI is used in GxP environments.
AI Controls Should Depend on Capability, Integration and Risk
The RAPS discussion also highlighted the importance of distinguishing AI systems according to their actual technical characteristics.
Not every AI system creates the same regulatory problem.
Relevant differences include:
- static versus adaptive models;
- deterministic versus probabilistic outputs;
- task-specific models versus general-purpose foundation models;
- standalone models versus AI embedded in larger systems;
- advisory AI versus systems capable of initiating actions;
- internally controlled versus externally hosted models.
The required control architecture should therefore depend not only on the label “AI”, but on what the technology actually does.
This supports a risk-based sequence such as:
Intended Use → Context of Use → Technical Characteristics → Decision Impact → Risk → Controls → Evidence
What Pharmaceutical Companies Should Do Now
Companies do not need to wait for complete international harmonisation before establishing sensible AI governance.
Several foundations are already becoming consistent across FDA and EMA thinking.
A practical approach should include:
- an inventory of AI applications;
- clearly defined intended use and Context of Use;
- GxP and regulatory-impact classification;
- documented data provenance;
- risk-based performance requirements;
- independent testing where appropriate;
- human oversight;
- model and configuration version control;
- change management;
- ongoing performance monitoring;
- defined reassessment and revalidation triggers.
The future technical standards may change some details.
The fundamental control principles are already becoming relatively clear.
Why This Development Matters
The most important signal from RAPS Convergence is not a new regulation.
It is the scale of actual regulatory experience.
More than 1,500 AI-related submissions means that AI is increasingly entering real regulatory dossiers rather than remaining a theoretical future technology.
At the same time, FDA and EMA increasingly share the same high-level principles.
The next challenge is therefore:
turning regulatory principles into internationally consistent technical evidence standards.
For pharmaceutical companies, this suggests that successful AI governance should be designed with global convergence in mind rather than built independently for each jurisdiction.
Regulatory Status Note
The statements reported from RAPS Convergence represent discussion by regulators and industry experts and do not constitute new FDA or EMA guidance.
The figure of more than 1,500 AI-related submissions was reported by an FDA CDER representative during the RAPS Convergence panel.
The FDA/EMA Guiding Principles of Good AI Practice in Drug Development are official joint regulatory principles, but they do not replace applicable legislation, GxP requirements or application-specific regulatory guidance.
Sources
RAPS – Convergence: Regulators emphasize need for harmonized approach to AI, published 21 September 2026:
https://www.raps.org … -approach-to-ai.html
FDA – Guiding Principles of Good AI Practice in Drug Development:
https://www.fda.gov/ … ice-drug-development
EMA/FDA – Guiding Principles of Good AI Practice in Drug Development:
https://www.ema.euro … g-development_en.pdf
FDA – Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products: