Artificial intelligence

The European medicines regulatory network aims to enable regulatory systems in the European Union (EU) to use the capabilities of artificial intelligence (AI) while managing its risks. Capabilities include personal productivity, process automation, better insights into data and decision-making support for the benefit of public and animal health.

Added on 29 September 2025:
'AI research priorities survey' section

Artificial intelligence (AI) is key to leveraging large volumes of regulatory and health data as well as new tools. This will encourage research and innovation. It will also support regulatory decision-making for safe, effective and high-quality medicines that reach patients faster.

This approach is in line with the European medicines regulatory network's plans on how to make use of AI. 

It is also documented in the Network Data Steering Group's workplan for 2025-2028.

The workplan identifies actions in four key AI-related areas:

• Guidance, policy and product support - delivering guidance on the use of AI throughout the medicine lifecycle
• Tools and technology - providing frameworks for the use of AI tools
• Collaboration and change management - developing capacity for the use of AI technology, and informing and preparing regulators for the AI transformation
• Experimentation - ensuring a structured and coordinated approach

This workplan integrates and expands on the AI workplan set up by the former Big Data Steering Group. This document is available in the 'Related documents' section on this page.

For more information, see:

• Data in regulation: Big data and other sources
• Network Data Steering Group: Workplan

AI research priorities survey

The joint HMA-EMA Network Data Steering Group (NDSG) is running an EU survey to inform research priorities for AI use in the development and evaluation of medicines.

To access the survey, please click on the link below:

• AI in Medicine Survey - Stakeholder priorities to inform research and regulatory agenda

Stakeholders invited to participate in this survey include:

• the European medicines regulatory network;
• academics;
• pharmaceutical industry;
• small and medium-sized enterprises (SMEs);
• patients and consumers;
• healthcare professionals and their organisations.

Survey feedback will be used to identify further research areas that can support safe and effective use of AI in medicine. 

The priority areas will be shared with the broader research community to advance academic and multi-stakeholder research in the AI field.

The deadline to submit feedback is 17 October.

For more information, see:

• Network Data Steering Group (NDSG)

AI in medicinal product lifecycle reflection paper

A reflection paper on the use of artificial intelligence (AI) in the medicinal product lifecycle is available:

• The use of Artificial Intelligence (AI) in the medicinal product lifecycle

It includes considerations to help medicine developers and marketing authorisation applicants use AI and machine learning in a safe and effective way at the different stages of a medicine lifecycle.

This paper reflects EMA's current experience in the evolving field of AI.

Medicine developers and applicants should understand the paper's considerations in the context of EU legal requirements and principles on AI, data protection, and medicines regulation.

The Methodology Working party developed the paper with support from the former Big Data Steering Group. 

The Committee for Human Medicinal Products (CHMP) and the Committee for Veterinary Medicinal Products (CVMP) both adopted the paper in September 2024.

Large language model guiding principles

Guiding principles are available for European medicines regulatory network staff on how to use large language models in their work. 

This document aims to promote the safe, responsible and effective use of this category of artificial intelligence (AI) technology.

Large language models focus on text generation. 

They can help staff at medicine regulators across the EU in areas such as processing extensive documentation, automating data mining and optimising routine administrative tasks.

They also present challenges such as providing irrelevant or inaccurate responses and posing potential data security risks. 

The overarching purpose of these guiding principles is to convey both capabilities and limitations of large language models.

Guiding principles

The document covers the following principles and recommendations on the use of large language models:

• Ensuring safe input of data;
• Applying critical thinking and cross-checking outputs;
• Upholding continuous learning;
• Knowing whom to consult when concerns arise.

EMA and the Heads of Medicines Agency (HMA) published the first version of this document on September 2024. It is regularly updated.

Read guiding principles

Factsheet: Four principles for safe and responsible use of large language models

Learn more on how to use large language model applications effectively while avoiding potential risks.

AI Observatory report and horizon scanning

The first annual report of the European medicines regulatory network's AI Observatory is available, including the results of a horizon scanning.

The report compiles the Network's experience with artificial intelligence (AI) during 2024 in:

• enhancing productivity;
• automating tasks;
• supporting data-driven decisions across a medicine lifecycle.

In addition to the report, two other related documents are also available:

• a compilation of examples of AI use in medicine regulation;
• a horizon scanning short report.

The horizon scanning report was based on the review of scientific literature and EU-funded projects. It helps identifying gaps, challenges and opportunities for integrating AI in medicine regulation.

The observatory captures and shares experiences and trends in AI to inform the work of the Network Data Steering Group. It ultimately supports the implementation of the group's multi-annual AI workplan.

2024 AI Observatory report

Reference Number: EMA/76534/2025

2024 AI Observatory report: Compilation of 2024 experience

Reference Number: EMA/154528/2025

Review of artificial intelligence and machine learning applications in medicines lifecycle 2024: Horizon Scanning Short Report

Reference Number: EMA/571739/2024

AI tools and technology

In March 2024, EMA introduced an AI-enabled knowledge mining tool called Scientific Explorer for EU regulators.

The tool enables easy, focused and precise search of regulatory scientific information from network's sources to support decision-making and simplify processes.

The first releases focuses on scientific advice procedures for human medicines and veterinary medicines.

For more information, see the document below with answers to frequently asked questions about the tool:

Scientific Explorer - Frequently Asked Questions

Reference Number: EMA/126785/2024

First qualification opinion on AI methodology

EMA’s human medicines committee (CHMP) accepts clinical trial evidence generated by an artificial intelligence (AI) tool supervised by a human pathologist. 

This is the first time EMA will consider data generated with the assistance of an AI-based tool to be scientifically valid.

This tool helps pathologists analyse liver biopsy scans to determine the severity of metabolic dysfunction associated steatohepatitis (MASH). This was formerly known as non-alcoholic steatohepatitis (NASH). The previous denomination gives the tool its name: AIM-NASH. 

It helps researchers obtain clearer evidence on the benefits of treatments in clinical trials with fewer patients.

EMA reached this milestone after its human medicines committee (CHMP) issued a qualification opinion on this AI-based innovative development methodology, in March 2025.

The qualification opinion was available for public consultation between December 2024 and January 2025.

Qualification opinion for artificial intelligence-based measurement of non-alcoholic steatohepatitis histology in liver biopsies to determine disease activity in NASH/MASH clinical trials

Adopted Reference Number: EMADOC-1700519818-1761332 Corr.1

Overview of comments received on the draft qualification opinion for artificial intelligence-based measurement of non-alcoholic steatohepatitis histology (AIM-NASH) tool