A new paper published on 13 August 2026 in Clinical Pharmacology & Therapeutics provides an important indication of where future regulatory science for artificial intelligence in medicines may be heading.
The study, Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance, was developed within the European medicines regulatory network and involved authors affiliated with the European Medicines Agency and several European regulatory organisations.
The researchers asked a particularly important question:
Which scientific problems need to be solved to enable trustworthy use of AI throughout the medicines lifecycle?
The results suggest that stakeholders are increasingly looking beyond general AI governance principles.
The major unresolved challenge is becoming much more practical:
How can we demonstrate that AI-generated results are sufficiently accurate, reliable, robust and trustworthy for regulated pharmaceutical use?
273 Stakeholders Across the Medicines Ecosystem
The study was based on a European-wide survey conducted by the Network Data Steering Group of the European Medicines Regulatory Network.
A total of 273 responses were collected from groups including:
- national competent authorities;
- pharmaceutical industry professionals;
- small and medium-sized enterprises;
- patients and consumers;
- healthcare professionals;
- contract research organisations;
- academic researchers;
- EU agency professionals.
The largest groups were national competent authority employees, representing 24% of respondents, and pharmaceutical industry professionals, representing 22%.
Importantly, most respondents already had at least some professional familiarity with AI.
The study therefore provides more than a general public perception of AI. It captures views from people directly involved in medicines development, regulation and evaluation.
Seven Areas of AI Regulatory Science Were Evaluated
The researchers identified 28 research questions grouped into seven domains:
- accuracy and reliability of AI tools;
- data governance, confidentiality and consent;
- ethics, fairness and bias prevention;
- regulation and oversight;
- research integrity and intellectual property;
- resources and support for AI use;
- impact on jobs and skills.
Respondents were asked to rank the most important challenges.
The result was particularly interesting.
Accuracy and reliability of AI tools emerged as the highest-priority domain by a substantial margin.
The Number-One Question: Can We Trust the AI Output?
The highest-ranked research question concerned how the accuracy and reliability of AI models can be ensured when they generate evidence or inform regulatory decisions, and how their limitations can be identified.
Within the accuracy and reliability domain, this question was ranked highest by 45% of respondents.
The second major issue was closely related:
How can AI remain robust and trustworthy when data changes, performance deteriorates over time, information is incomplete or inputs are misleading?
This brings several familiar AI lifecycle risks directly into the regulatory science discussion:
- data drift;
- model drift;
- performance degradation;
- incomplete information;
- unexpected inputs;
- misleading inputs;
- uncertainty;
- changing operating environments.
The implication for pharmaceutical companies is important.
Initial AI validation may demonstrate acceptable performance at one point in time, but it does not automatically demonstrate that performance will remain acceptable throughout the system lifecycle.
From Validation to Maintaining an AI State of Control
This finding is particularly relevant for GMP applications.
Traditional computerized system validation often asks:
Did the system meet its predefined requirements when it was tested?
For AI, another question becomes equally important:
Does the system continue to perform reliably under changing conditions?
This leads naturally toward lifecycle controls such as:
- ongoing performance monitoring;
- predefined performance limits;
- drift detection;
- periodic re-evaluation;
- change control;
- model version control;
- revalidation or requalification triggers;
- monitoring of failure modes.
The regulatory science priorities identified in the study therefore support a broader concept of AI assurance:
Validation should not only demonstrate initial fitness for intended use. It should also establish how continued fitness for use will be demonstrated.
For GMP applications, this is closely related to the emerging concept of maintaining AI in an algorithmic state of control.
Explainability Is Important – But the Question Is “When and How Much?”
The third priority within the accuracy and reliability domain concerned explainability.
The study asks:
When is AI explainability important for regulatory decision-making, and which methods can provide it without unnecessarily affecting model performance?
This is an important distinction.
The future regulatory expectation may not necessarily be:
Every AI model must always be completely explainable.
A more risk-based question could be:
What level of explainability is necessary for this particular intended use, decision and associated risk?
For example, explainability requirements could reasonably differ between AI used to:
- search regulatory information;
- identify possible trends;
- support deviation investigations;
- predict process behaviour;
- support product quality decisions;
- generate evidence used in regulatory submissions.
This would be consistent with a context-of-use and risk-based approach to AI assurance.
Data Governance Is the Second Major Priority
The second-highest overall research domain was:
Data governance, confidentiality and consent.
Among the important questions identified were how data used for AI-based medicines development can be made:
- secure;
- auditable;
- traceable;
- legally and ethically usable.
For pharmaceutical companies, this reinforces a fundamental principle:
AI assurance starts with assurance of the data used by AI.
An advanced AI model cannot compensate for data that are incomplete, poorly controlled, untraceable or inappropriate for the intended purpose.
In GxP environments this connects directly with established expectations for:
- data integrity;
- data provenance;
- traceability;
- security;
- access control;
- retention;
- auditability.
Bias and Transparency Remain Major Concerns
Ethics, fairness and bias prevention ranked third among the seven domains.
The study identified two particularly important questions:
- How can bias in AI models used in medicines development and evaluation be identified and reduced?
- How should the limitations and uncertainties of AI use be communicated transparently?
The second point may be particularly important in regulated environments.
AI output should not create an impression of certainty that the underlying model cannot justify.
A mature pharmaceutical AI system may therefore need mechanisms for communicating:
- uncertainty;
- limitations;
- confidence;
- conditions under which the model should not be used;
- situations requiring escalation to a human expert.
A Surprising Result: “More Regulation” Was Not the Highest Priority
One of the most interesting findings is that Regulation and oversight ranked only fourth among the seven domains.
This should not be interpreted as suggesting that regulation is unimportant.
The authors propose another possible explanation.
Horizontal frameworks such as the EU Artificial Intelligence Act already provide important elements of AI governance.
What remains unresolved are many of the scientific and methodological questions that legislation alone cannot answer.
For example:
How accurate is accurate enough?
How should robustness be demonstrated?
How should performance degradation be detected?
When is explainability necessary?
What validation methodology should be used?
How should uncertainty be communicated?
These are fundamentally different questions from simply determining whether an AI system is legally permitted.
For pharmaceutical companies, this distinction is important.
The next major challenge may not be another general AI policy. It may be the development of accepted scientific methods for demonstrating trustworthy AI performance.
The Ten Highest Regulatory Research Priorities
After weighting the research questions according to the importance of their respective domains, the study identified ten priority areas.
They concern:
- accuracy and reliability of AI-generated evidence;
- robustness under changing data, performance degradation and misleading inputs;
- appropriate AI explainability;
- legal basis and consent for secondary use of health data;
- security, auditability and traceability of AI data;
- identification and reduction of AI bias;
- transparent communication of AI limitations and uncertainties;
- gaps in guidance for benefit-risk assessment, pharmacovigilance and evidence generation;
- transparency and reproducibility of AI-enabled research;
- technical checks and quality controls required to ensure that AI systems are validated, secure and reliable.
One Priority Is Directly Relevant to Pharmaceutical Manufacturing
The tenth research priority is especially important from a GMP perspective.
The study asks which:
technical checks and quality controls are essential to ensure that AI systems used in clinical trials, manufacturing, safety monitoring and regulatory submissions are properly validated, secure and reliable.
This explicitly places manufacturing within the regulatory science agenda for AI assurance.
The question goes beyond conventional software validation.
For an AI application in pharmaceutical manufacturing, appropriate control could potentially require consideration of:
- intended use;
- model performance;
- independent test data;
- data integrity;
- robustness;
- security;
- human oversight;
- model changes;
- performance monitoring;
- drift;
- revalidation triggers;
- failure management.
These are likely to become increasingly important as AI moves from experimental applications toward routine GxP processes.
The Real Question Is Moving from “Can We Use AI?” to “Can We Trust It?”
Perhaps the most important conclusion of the paper is captured by the overall pattern of the results.
Stakeholders do not appear primarily concerned with whether artificial intelligence should be used in medicines development.
The more important question is:
How can AI-generated outputs be trusted when they influence evidence generation and regulatory decision-making?
This represents an important evolution of the pharmaceutical AI discussion.
The early discussion was largely:
Can AI be used in regulated pharmaceutical processes?
The emerging discussion is increasingly:
What evidence is necessary to demonstrate that AI is sufficiently trustworthy for a particular regulated use?
That requires moving from generic principles toward measurable scientific criteria.
What This Means for Pharmaceutical Companies
For pharmaceutical organisations developing AI governance frameworks, the study provides a useful indication of where attention should be focused.
A practical AI assurance model could increasingly be built around:
Intended Use → Risk → Data → Performance → Robustness → Explainability → Controls → Human Oversight → Change Control → Continuous Monitoring
This is broader than traditional computerized system validation.
It also suggests that the future of AI assurance in pharma may be less about creating one universal “AI validation procedure” and more about establishing a multi-layered control strategy appropriate to the intended use and associated risk.
The fundamental regulatory science question therefore becomes:
Can we demonstrate – with evidence – that this AI remains sufficiently reliable, secure and controlled for the decision or process it supports?
Regulatory Status Note
This publication is a scientific research article and does not constitute formal EMA guidance or a new regulatory requirement.
The authors explicitly state that the views expressed are their personal views and should not be interpreted as representing the official position of the regulatory agencies or organisations with which they are affiliated.
The study should therefore be understood as an important indicator of regulatory science priorities rather than as an enforceable regulatory framework.
Nevertheless, its importance is increased by the involvement of the European Medicines Regulatory Network and by the participation of regulators, pharmaceutical industry professionals and other stakeholders.
Source
Pinheiro L.C. et al. – Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance. Clinical Pharmacology & Therapeutics. First published 13 August 2026.
https://ascpt.online … oi/10.1002/cpt.70400
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