AIforPharma.org

Introduction

⇒ HMA and EMA will hold a multistakeholder AI and Data Forum in November 2026 focused on building trustworthy AI through strong data foundations, governance, quality and interoperability across the medicines lifecycle.

The European medicines regulatory network is putting data quality and governance at the centre of its artificial intelligence strategy.

On 12–13 November 2026, the Heads of Medicines Agencies (HMA) and European Medicines Agency (EMA) will hold a new multistakeholder AI and Data Forum titled:

Build trustworthy AI on strong data foundations.

The event (with the public live stream) will bring together regulators and stakeholders to discuss the use of artificial intelligence and data across the medicines lifecycle.

The title itself sends an important message.

The regulatory discussion around pharmaceutical AI is increasingly moving beyond the question:

“Is the AI model sufficiently accurate?”

towards a broader question:

“Are the data, governance and infrastructure surrounding the AI sufficiently trustworthy to support regulated use?”

AI and Data Are Being Treated as One Regulatory Problem

EMA describes data and AI as increasingly interdependent.

High-quality data provide the foundation for scalable and trustworthy AI, while AI can in turn help organisations obtain more value from increasingly large and complex regulatory and health datasets.

This creates an important relationship:

Data Quality → Data Governance → Interoperability → AI Performance → Regulatory Trust

For pharmaceutical companies, this means that AI governance cannot be separated from data governance.

A technically advanced model may still be unsuitable for a regulated use if the underlying data are:

• incomplete;
• poorly structured;
• inconsistently defined;
• not sufficiently traceable;
• not representative of the intended use;
• difficult to exchange between systems;
• of uncertain provenance.

In other words:

better AI cannot compensate automatically for weak data foundations.

What the HMA/EMA Forum Will Address

According to EMA, the forum has several objectives.

It will:

• showcase current AI and data applications across the medicines lifecycle;
• discuss the evolving European regulatory landscape for AI and data;
• examine data foundations, governance, quality and interoperability;
• strengthen collaboration between regulators and stakeholders;
• provide input to the Network Data Steering Group workplan and the European medicines network strategy to 2028.

This is broader than a conventional AI workshop.

The focus is not only on algorithms.

It includes the regulatory data infrastructure required to make AI reliable, scalable and useful.

The Forum Is Part of a Larger HMA/EMA AI Strategy

The event is coordinated by the HMA/EMA Network Data Steering Group (NDSG), together with the CHMP Methodology Working Party and the Accelerating Clinical Trials in the EU initiative.

The NDSG workplan for 2026–2028 is particularly important background.

The workplan defines how the European medicines regulatory network intends to expand the use of data and AI in medicines regulation.

Planned activities include:

• development of data analytics and data standardisation frameworks;
• further development of real-world evidence infrastructure;
• implementation of the European Health Data Space;
• expansion of access to regulatory data assets;
• development of AI guidance for clinical development and pharmacovigilance;
• regulatory experimentation and capability building.

AI is therefore becoming part of a broader European regulatory data strategy rather than remaining a standalone technology initiative.

Why Data Standardisation Matters for AI

Artificial intelligence performs best when information is consistently structured and interpretable.

Pharmaceutical organisations, however, often operate complex data environments involving:

• laboratory systems;
• manufacturing systems;
• quality-management systems;
• clinical databases;
• regulatory repositories;
• pharmacovigilance systems;
• master-data platforms;
• unstructured documents.

The same concept may be represented differently across sites, systems or countries.

For example, products, processes, deviations, equipment, substances or regulatory events may use different terminology and metadata structures.

For conventional reporting this already creates difficulties.

For AI it becomes even more important.

An AI model can analyse large volumes of information, but if the underlying information does not have consistent meaning, the model may connect data incorrectly or generate misleading conclusions.

This is why interoperability and standardisation are increasingly becoming AI topics.

Data Provenance May Become as Important as Model Performance

For regulated pharmaceutical AI, it may not be enough to know that a model generated a particular result.

The organisation may also need to understand:

• where the underlying data originated;
• which version of the data was used;
• how the data were transformed;
• whether the source was controlled;
• whether important information was excluded;
• whether the data remain representative of the intended use.

This can be described as data provenance.

For some AI applications, confidence in the output will depend directly on confidence in this data lineage.

A useful assurance chain may therefore become:

Source Data

↓

Controlled Transformation

↓

Verified Dataset

↓

AI Model

↓

AI Output

↓

Human / Regulatory Decision

Each layer can affect the credibility of the final result.

Trustworthy AI Is Not Only a Model Validation Question

Much of the pharmaceutical AI discussion has focused on validation of the model.

Important questions include:

• Is the model sufficiently accurate?
• How should independent test data be selected?
• How should robustness be demonstrated?
• How should drift be monitored?
• When should revalidation occur?

These questions remain important.

But the HMA/EMA forum highlights another dimension.

Even a well-performing model depends on a broader control environment.

A mature AI assurance framework therefore needs to consider:

Data → Model → System → User → Process → Decision

rather than validating the algorithm in isolation.

Data Governance Becomes Part of AI Governance

For pharmaceutical companies, data governance may increasingly become one of the main components of AI governance.

Relevant controls can include:

• data ownership;
• defined data sources;
• data-quality criteria;
• metadata standards;
• master-data governance;
• data lineage;
• access control;
• retention;
• security;
• change management;
• interoperability standards.

This is especially important when AI applications combine information from multiple systems.

For example, an AI system supporting Quality Management could potentially analyse:

• deviations;
• CAPAs;
• complaints;
• audit findings;
• laboratory results;
• manufacturing trends;
• supplier information.

The value of the AI may depend less on the sophistication of the language model and more on whether these sources can be connected reliably.

AI Regulatory Strategy Is Moving Toward Infrastructure

This forum also illustrates an important evolution in regulatory thinking.

Earlier AI discussions frequently concentrated on broad principles such as:

• transparency;
• fairness;
• explainability;
• human oversight;
• accountability.

Those principles remain important.

However, regulators are increasingly moving toward practical implementation questions:

• Which data should AI use?
• How should those data be governed?
• How should data quality be demonstrated?
• How can different systems exchange information reliably?
• How can AI operate consistently across a regulatory network?

This is a transition from:

AI principles

towards:

AI infrastructure and regulatory implementation.

A Strong Message for Pharmaceutical Companies

For companies developing AI strategies, the practical implication is straightforward.

The AI programme should not start only with:

“Which AI model should we buy?”

It should also ask:

• Do we know which data the AI will use?
• Are those data sufficiently controlled?
• Are definitions consistent across systems?
• Can the source of every important data element be reconstructed?
• Are data-quality problems visible?
• Can information be exchanged between systems without losing context?
• Who owns the data?

This can lead to a more realistic implementation sequence:

Intended Use

↓

Data Requirements

↓

Data Quality and Governance

↓

AI Model and Architecture

↓

Validation / Credibility Assessment

↓

Controlled Deployment

↓

Lifecycle Monitoring

Relevance to GMP

The November forum is not specifically a GMP workshop and should not be interpreted as new GMP guidance.

However, its central message is highly relevant to GMP applications of AI.

Existing GMP expectations already place strong emphasis on:

• data integrity;
• traceability;
• controlled records;
• validated systems;
• defined responsibilities;
• change control.

AI does not reduce the importance of these principles.

It can increase it.

When an AI system derives conclusions from thousands or millions of data points, understanding whether those data are reliable becomes fundamental to understanding whether the AI output can be trusted.

For GMP AI, one useful principle may therefore be:

The validated state of an AI-enabled process depends not only on the model, but also on the continued state of control of its data.

What to Watch During the November Forum

Several areas will be particularly worth following:

• how EMA and HMA define trustworthy AI in practical terms;
• expectations for data quality supporting AI;
• approaches to data interoperability and standardisation;
• examples of AI use across the medicines lifecycle;
• the relationship between AI governance and the evolving EU regulatory framework;
• future NDSG workplan priorities;
• possible new AI guidance or regulatory initiatives.

The forum may therefore provide an important indication of the next phase of European pharmaceutical AI regulation.

The first phase focused heavily on AI principles.

The next phase may increasingly focus on:

data foundations → practical governance → implementation → regulatory evidence.

Participation

The HMA/EMA Multistakeholder AI and Data Forum will take place on:

12–13 November 2026.

It will be held at the European Medicines Agency in Amsterdam and online.

EMA states that online participation is available through Webex registration.

In-person participation is by invitation through official stakeholder groups.

The event will also be broadcast live.

Regulatory Status Note

The HMA/EMA AI and Data Forum is a multistakeholder regulatory workshop and does not itself establish new regulatory requirements.

Its discussions and presentations should not be interpreted as formal EMA guidance unless subsequently incorporated into official regulatory documents.

However, the forum is directly linked to the HMA/EMA Network Data Steering Group and its 2026–2028 workplan, making it an important indicator of the future direction of European medicines regulation in data and artificial intelligence.

Sources

European Medicines Agency – HMA / EMA multistakeholder AI and Data forum: Build trustworthy AI on strong data foundations:

https://www.ema.euro … ong-data-foundations

EMA – Network Data Steering Group:

https://www.ema.euro … -steering-group-ndsg

EMA – NDSG Workplan 2026–2028: Data and Artificial Intelligence in Medicines Regulation:

https://www.ema.euro … es-regulation_en.pdf

EMA – Artificial Intelligence:

https://www.ema.euro … ificial-intelligence

Introduction

⇒ FDA reports more than 1,500 AI-related drug submissions as FDA and EMA regulators call for greater international harmonisation of AI standards, lifecycle controls and evidence requirements across pharmaceutical development.

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:

https://www.fda.gov/ … -drug-and-biological

The UK Medicines and Healthcare products Regulatory Agency has opened a new initiative focused specifically on the use of artificial intelligence in medicines safety evaluation.

On 16 September 2026, MHRA launched the Beyond ADMET: AI for Medicines Safety stakeholder survey.

The initiative will support development of a future regulatory sandbox examining how AI could be used to improve prediction and assessment of medicines safety.

This is important because the regulator is not simply publishing general principles for responsible AI.

MHRA is actively asking pharmaceutical, biotechnology, toxicology and AI organisations:

What prevents AI models from being used confidently in regulatory medicines-safety evaluation?

Moving Beyond Classical ADMET Assessment

ADMET traditionally refers to:

• Absorption;
• Distribution;
• Metabolism;
• Excretion;
• Toxicity.

These characteristics are fundamental to understanding how a medicine behaves in the body and whether potential safety problems may occur.

Traditional safety evaluation relies on multiple sources of evidence, including:

• preclinical studies;
• toxicology;
• clinical trials;
• pharmacokinetic information;
• post-marketing information.

MHRA’s initiative explores whether AI can integrate a broader range of information, including traditional preclinical data together with clinical and real-world data.

The objective is to improve understanding of how medicines behave:

• across different populations;
• in different disease states;
• under different clinical conditions;
• in real-world use.

Potentially, this could help identify safety risks earlier.

The Regulator Is Explicitly Asking About Validation

One of the most important aspects of the initiative is the scope of the questions MHRA wants industry and researchers to address.

The Agency specifically identifies areas including:

• model development;
• validation;
• access to data;
• data sharing;
• regulatory considerations.

This is significant.

The central problem with AI in regulated pharmaceutical science is increasingly not whether sophisticated models can be built.

The more difficult question is:

What evidence is necessary before the regulator can rely on their output?

That requires moving from model development toward model credibility.

AI Performance Alone Is Not Enough

A highly accurate AI model may still be unsuitable for regulatory use if the organisation cannot adequately explain:

• what data were used;
• which population the data represent;
• how the model was validated;
• where its limitations are;
• whether performance generalises beyond the development dataset;
• how model changes are controlled.

This is particularly important for medicines safety.

A model can perform well overall while performing poorly for a specific:

• patient population;
• disease state;
• rare adverse event;
• drug class;
• clinical context.

Average performance therefore does not necessarily demonstrate regulatory credibility.

Data May Be the Bigger Problem Than the Algorithm

MHRA specifically identifies access to data and data sharing as areas of interest.

This reflects a broader problem in pharmaceutical AI.

Medicines-safety information is distributed across multiple sources:

• preclinical datasets;
• clinical trials;
• electronic health records;
• pharmacovigilance systems;
• real-world evidence;
• literature;
• laboratory data.

Connecting those datasets creates significant challenges involving:

• data quality;
• standardisation;
• missing data;
• population bias;
• privacy;
• interoperability;
• provenance.

A sophisticated AI model cannot compensate automatically for poor or non-representative data.

This leads to an increasingly important regulatory principle:

AI model credibility begins with data credibility.

Why a Regulatory Sandbox Is Important

The stakeholder survey will inform development of the Beyond ADMET regulatory sandbox, which MHRA announced earlier in 2026.

A regulatory sandbox is different from conventional guidance development.

Instead of attempting to define all regulatory expectations in advance, a sandbox allows regulators and innovators to explore real technologies and identify regulatory problems through practical cases.

This can help answer questions such as:

• What evidence is sufficient?
• Which validation approaches are practical?
• How should uncertainty be communicated?
• What documentation does a regulator need?
• How should model changes be controlled?
• Which data-quality limitations are acceptable?

For rapidly developing AI technologies, this iterative regulatory approach may be particularly valuable.

The Important Shift: From AI Principles to Regulatory Evidence

Over the last several years, many AI frameworks have converged around principles such as:

• human oversight;
• transparency;
• risk-based governance;
• data quality;
• lifecycle management.

Those principles are important.

But pharmaceutical regulation eventually requires something more concrete:

evidence.

The question becomes:

What evidence must a company provide to demonstrate that an AI model is sufficiently reliable for a particular regulatory purpose?

The Beyond ADMET initiative appears to be moving directly toward that problem.

This Is Closely Related to Context of Use

The acceptable level of AI uncertainty depends heavily on how the model is used.

For example, AI could be used to:

• prioritise compounds for additional evaluation;
• identify potential safety signals;
• support toxicology assessment;
• integrate multiple data sources;
• predict possible adverse effects;
• support regulatory evidence.

These applications do not carry identical consequences.

The validation strategy should therefore reflect:

Intended Use → Context of Use → Decision Impact → Required Credibility

The closer the AI output is to a consequential regulatory or safety decision, the stronger the evidence supporting its reliability is likely to need to be.

Lifecycle Control Will Also Matter

AI models do not necessarily remain static.

Performance can change because of:

• new data;
• changing populations;
• model retraining;
• algorithm updates;
• software changes;
• changes in clinical practice.

This creates another important regulatory question:

When does a change require reassessment or revalidation?

For regulated pharmaceutical AI, initial validation cannot necessarily be treated as the end of the assurance process.

A more realistic model may become:

Model Development

↓

Validation

↓

Defined Context of Use

↓

Deployment

↓

Performance Monitoring

↓

Change Assessment

↓

Revalidation Where Necessary

Why This Matters Beyond Safety Prediction

The Beyond ADMET programme is not GMP guidance.

However, the regulatory questions being investigated are closely related to questions facing AI throughout pharmaceutical development and manufacturing:

• How should AI models be validated?
• What data are acceptable?
• How should model limitations be documented?
• How should uncertainty be managed?
• How should changes be controlled?
• When can a regulator rely on AI-generated evidence?

These questions are also relevant to AI used in:

• manufacturing models;
• quality systems;
• process monitoring;
• analytical applications;
• regulatory submissions.

The specific application changes, but the underlying credibility problem remains similar.

MHRA Is Asking Industry for Practical Experience

Importantly, MHRA is not limiting participation to organisations already using advanced AI systems.

The Agency is seeking input from organisations across the medicines-development pathway, including:

• toxicology;
• preclinical research;
• clinical research;
• life sciences;
• biotechnology;
• AI technology.

Organisations can contribute experience concerning current use, future plans, barriers and unmet needs.

The survey remains open until:

22 December 2026 at 23:59 UK time.

For pharmaceutical companies developing AI-based safety models, this represents a direct opportunity to influence the regulator’s understanding of practical validation and data challenges.

Why This Development Is Important

The Beyond ADMET initiative is another indication that regulators are moving from general AI policy toward application-specific regulatory science.

The progression can be seen as:

AI Principles

↓

Specific Use Cases

↓

Regulatory Sandboxes

↓

Evidence and Validation Methods

↓

Future Regulatory Expectations

This is a significant development for pharmaceutical companies.

Future AI regulation may not consist of one universal AI validation standard.

Instead, regulators may develop different evidence expectations for different contexts of use.

Regulatory Status Note

The Beyond ADMET stakeholder survey is an MHRA call for evidence.

It does not establish a new regulatory requirement and does not represent final guidance on validation or acceptance of AI models for medicines-safety evaluation.

Its findings will inform development of the Beyond ADMET regulatory sandbox and future MHRA work in this area.

The initiative should therefore be viewed as an important regulatory-development signal rather than a current compliance requirement.

Source

MHRA – Beyond ADMET: AI for medicines safety stakeholder survey, published 16 September 2026:

https://www.gov.uk/g … y-stakeholder-survey