The European medicines regulatory network is preparing a more coordinated framework for artificial intelligence across the medicines lifecycle.
Summary notes published by EMA on 14 October 2026 confirm several important developments:
- a newly established AI Guidance Coordination Group;
- planned horizontal guidance on good machine-learning practice;
- dedicated AI guidance for clinical development;
- continued EMA–FDA work on common AI terminology;
- greater attention to AI-ready regulatory data infrastructure.
These developments are strategically important because pharmaceutical AI guidance has so far emerged through several separate initiatives.
The new direction is toward:
one coordinated regulatory architecture combining horizontal AI principles with domain-specific guidance.
What Is the AI Guidance Coordination Group?
The new AI Guidance Coordination Group has been established within the European medicines regulatory network.
Its purpose is to coordinate AI-related guidance activities across the medicines lifecycle.
The Group will:
- maintain an overview of ongoing AI guidance initiatives;
- help align their scope and planning;
- identify overlaps and gaps;
- support coordination with the European Commission;
- promote a coherent regulatory approach.
The Group will not itself draft or approve guidance.
It will also not replace the responsibilities of existing scientific committees or working parties.
Its function is therefore coordination rather than regulatory decision-making.
Why Coordination Is Needed
AI can be used across many pharmaceutical functions, including:
- drug discovery;
- nonclinical modelling;
- clinical development;
- manufacturing;
- pharmacovigilance;
- regulatory assessment;
- post-authorisation monitoring.
Different expert groups may need to develop guidance for these different domains.
Without coordination, several problems could arise:
- duplicated requirements;
- inconsistent terminology;
- different interpretations of risk;
- gaps between lifecycle stages;
- conflicting expectations for the same technology.
The Coordination Group is intended to reduce these risks.
Horizontal Guidance on Good Machine-Learning Practice
The October meeting discussed future horizontal guidance on good machine-learning practice.
EMA’s summary notes indicate that work is progressing on a horizontal document covering general machine-learning principles applicable across the medicines lifecycle.
This could become an important regulatory foundation.
A horizontal document may address principles that apply broadly regardless of the specific pharmaceutical use case, such as:
- Context of Use;
- data quality and provenance;
- model development;
- performance evaluation;
- robustness;
- transparency;
- human oversight;
- change control;
- lifecycle monitoring.
Domain-specific guidance could then explain how those principles apply in particular areas.
Horizontal and Domain-Specific Guidance Should Complement Each Other
A possible future structure may therefore look like:
Horizontal Machine-Learning Principles
↓
Clinical Development Guidance
Pharmacovigilance Guidance
GMP Annex 22
Other Domain-Specific Guidance
This structure could help avoid repeating the same principles in every regulatory document.
It could also create greater consistency across the pharmaceutical lifecycle.
Dedicated AI Guidance for Clinical Development
The European regulatory network is also preparing dedicated guidance on AI in clinical development.
EMA reported that a concept paper is currently being finalised and will be followed by public consultation.
The horizontal guidance and clinical-development guidance are intended to progress in parallel and complement each other.
This is significant because AI is increasingly used in clinical research for:
- patient selection;
- trial design;
- external control arms;
- digital endpoints;
- biomarkers;
- data analysis;
- safety monitoring.
These applications can influence evidence submitted for regulatory decision-making and therefore require a coherent framework.
Industry Has Identified Priority Areas Requiring Clarification
Industry participants identified several areas where additional regulatory clarity would be particularly valuable:
- risk categorisation and proportionality;
- validation and evidence requirements;
- third-party AI tools;
- documentation and transparency;
- lifecycle oversight of adaptive AI;
- regulatory submission expectations.
These issues reflect practical implementation challenges faced by pharmaceutical companies.
For example, a company may use a third-party AI platform without full visibility into:
- training data;
- model architecture;
- provider updates;
- model-change notifications;
- performance limitations.
Regulatory guidance will need to consider how a regulated company can remain accountable when important parts of the AI technology are externally controlled.
Validation Should Be Proportionate to Context of Use
The meeting also reinforced the importance of clearly defining use cases and Context of Use.
Evidentiary expectations may differ depending on the intended regulatory purpose.
For example, AI used to identify possible literature for human review does not necessarily require the same assurance as AI used to:
- define a clinical endpoint;
- generate an external control;
- support a benefit-risk conclusion;
- influence a critical product-quality decision.
A risk-based framework could therefore follow:
Context of Use → Decision Impact → Model Risk → Evidence Requirement
Synthetic Data, Digital Twins and AI-Derived Endpoints
The October meeting also considered emerging applications such as:
- synthetic and simulated data;
- external controls;
- digital twins;
- AI-derived endpoints;
- AI-derived biomarkers;
- AI-supported signal detection.
These technologies can potentially accelerate development and reduce dependence on conventional evidence-generation methods.
However, they also raise difficult regulatory questions.
For example:
- How should synthetic data be validated?
- How should uncertainty be quantified?
- When is a digital twin sufficiently credible?
- How should an AI-derived endpoint be qualified?
- How should bias and representativeness be assessed?
EMA noted that terminology, use cases and Contexts of Use need to be clearly defined.
AI-Ready Regulatory Data Infrastructure
Another major topic was data interoperability.
Industry participants emphasised that responsible AI depends on public regulatory information being:
- accessible;
- machine-readable;
- interoperable;
- reusable.
Examples discussed included information from:
- the Clinical Trials Information System;
- European Public Assessment Reports;
- product information;
- pharmacovigilance systems.
This is important because AI performance depends not only on the model but on the quality and structure of the information available to it.
Confidentiality and Sponsor Data Remain Important
The discussion also considered governance of sponsor-submitted data.
Making regulatory data more AI-ready must not mean that confidential or proprietary information can be reused without appropriate safeguards.
Relevant controls may need to address:
- confidentiality;
- authorised use;
- transparency;
- data ownership;
- protection of proprietary information.
This illustrates a recurring tension in pharmaceutical AI:
greater data accessibility can enable innovation, but regulated and confidential information still requires strong governance.
International Convergence Is a Major Priority
Industry participants strongly supported greater international cooperation.
Priority areas for convergence include:
- AI principles;
- terminology;
- risk assessment;
- validation in GxP environments;
- regulatory submission expectations.
EMA reported continued collaboration with FDA, including:
- development of a joint AI glossary;
- follow-up work related to the joint Guiding Principles of Good AI Practice;
- future discussions involving the European Commission, EMA and FDA.
International discussions are also taking place through the International Coalition of Medicines Regulatory Authorities.
Why a Joint AI Glossary Matters
Terminology may appear less important than validation, but inconsistent terminology can create real regulatory problems.
Terms such as:
- adaptive AI;
- dynamic model;
- synthetic data;
- digital twin;
- foundation model;
- human oversight
may be interpreted differently by companies and regulators.
A common glossary can therefore support:
- more consistent submissions;
- clearer guidance;
- better international alignment;
- reduced ambiguity during regulatory review.
Implications for Pharmaceutical Companies
The emerging EMA structure suggests that companies should avoid building separate AI governance approaches for every function.
A common enterprise framework can establish shared principles for:
- AI inventory;
- intended use;
- Context of Use;
- risk classification;
- data governance;
- model evaluation;
- human oversight;
- change management;
- performance monitoring.
Individual functions can then add requirements appropriate to clinical development, manufacturing, pharmacovigilance or other regulated areas.
What to Watch Next
Several future milestones will be important:
- publication of the clinical-development concept paper;
- opening of the public consultation;
- development of horizontal machine-learning guidance;
- progress on the EMA–FDA AI glossary;
- clarification of expectations for third-party and adaptive AI;
- alignment with draft GMP Annex 22.
The next meeting of the HMA–EMA AI industry-stakeholder group is scheduled for February 2027.
Why This Development Matters
The October meeting shows that European AI regulation for medicines is moving into a more structured phase.
The earlier phase focused largely on:
principles → reflection papers → individual guidance initiatives.
The next phase increasingly looks like:
Horizontal Guidance → Domain-Specific Guidance → International Convergence → AI-Ready Regulatory Infrastructure.
For pharmaceutical companies, this creates a clearer direction but also a broader governance challenge.
AI assurance will increasingly need to operate across the entire medicines lifecycle rather than within isolated technology projects.
Regulatory Status Note
The October meeting summary does not establish new legally binding requirements.
The horizontal machine-learning document and clinical-development guidance remain under development.
The clinical-development concept paper has not yet entered public consultation, and draft GMP Annex 22 remains a separate ongoing initiative.
The meeting should therefore be understood as an important regulatory-development signal rather than a current compliance standard.
Sources
EMA – Meeting of the HMA–EMA group focused on AI with industry stakeholders, October 2026:
https://www.ema.euro … holders-october-2026
EMA – Summary notes of the October 2026 meeting: