Data are generated and used increasingly across sectors, including those related to the lifecycle of medicines. In the healthcare sector, data are captured in electronic format on a routine basis.
The utilization of artificial intelligence (AI) - systems displaying intelligent behavior by analyzing data and taking actions with some degree of autonomy to achieve specific goals - is an important part of the digital transformation that enables increased use of data for analysis and decision-making.
Such systems are often developed through the process of machine learning (ML) where models are trained from data without explicit programming.
This reflection paper provides considerations on the use of AI and ML in the lifecycle of medicinal products, including medicinal products development, authorization, and post-authorization.
Given the rapid development in this field, the aim of this reflection paper is to reflect on the scientific principles that are relevant for regulatory evaluation when these emerging technologies are applied to support safe and effective development and use of medicines.
It is crucial to identify aspects of AI/ML that would fall within the remit of EMA or the National Competent Authorities of the Member States as the level of scrutiny into data during assessment will depend on this remit. This reflection paper focuses only on the use of AI in the medicinal product lifecycle.
Medical devices with AI/ML technology can be used within the context of clinical trials to generate evidence in support of a marketing authorization application and/or can be combined with the use of a medicinal product.
This reflection paper describes the current experience of EMA in a field where scientific knowledge is fast evolving. It should be read in coherence with both legal requirements and overarching EU principles on AI, data protection, and medicines regulation.
While some considerations in this reflection paper are of general interest for the development of veterinary medicinal products, important differences exist between the human and veterinary domain including legal bases, regulatory requirements and guidance, ethical issues, risks of bias and other sources of discrimination.
Further reflections will be necessary to better identify the specific circumstances and sources of bias in the veterinary setting. While veterinary medicines regulated by Regulation (EU) 2019/6 are generally within the scientific scope of this document, the reader is advised to pay attention to notes pointing out fundamental differences. Specific veterinary reflections or guidance may be developed in the future.
Read also: Artificial Intelligence in Machine Learning and Drug Product Development
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