Updated on 7 May 2025
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:
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:
A reflection paper on the use of artificial intelligence (AI) in the medicinal product lifecycle is available:
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.
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.
The document covers the following principles and recommendations on the use of large language models:
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
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:
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.