May 11 -- The Food and Drug Administration (FDA or Agency) is announcing the publication of a discussion paper entitled “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products.” To fulfill its mission of protecting, promoting, and advancing public health, FDA's Center for Drug Evaluation and Research (CDER), in collaboration with the Center for Biologics Evaluation and Research (CBER) and Center for Devices and Radiological Health (CDRH), including the Digital Health Center of Excellence (DHCoE), is issuing this document to facilitate a discussion with stakeholders on the use of artificial intelligence (AI) and machine learning (ML) in drug development to help inform the regulatory landscape in this area. Comments on the framework must be submitted by August 9, 2023.
FDA aims to ensure safety and effectiveness while facilitating innovations in the development of drugs. Recent rapid technological innovations in sophisticated data collection and generation tools, combined with robust information management and exchange systems, and advanced computing abilities may prove transformational in the way drugs are developed and used. This evolving ecosystem presents unique opportunities and challenges, and FDA is committed to working across its medical product centers with partners domestically and internationally to ensure that the full potential of these innovations is realized for the benefit of the public.
Developers, manufacturers, regulators, academic groups, and other stakeholders are working to develop a shared understanding of where and how specific innovations, such as AI and ML, can best be utilized across the drug development process, including through the use of AI/ML-enabled tools, which may include devices. FDA is publishing this discussion paper as part of a multifaceted approach to enhance mutual learning and to establish a dialogue with FDA stakeholders on this topic. While AI and ML are not consistently defined across all disciplines and stakeholders, AI can be generally described as a branch of computer science, statistics, and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is generally considered a subset of AI that allows ML models to be developed by ML training algorithms through analysis of data, without models being explicitly programmed. Additionally, there are a variety of ML methods and different types of algorithms that may be utilized in a given context. For the purposes of this discussion paper, AI and ML will be referenced together as AI/ML, and references to drug development and the drug development process include a wide scope of activities and phases, including manufacturing and surveillance, among others.
This discussion paper, which considers the application of AI/ML in the broad context of the drug development process, is not FDA guidance or policy, and is not meant to endorse a specific AI/ML use or approach in drug development. Rather, it is an initial communication with stakeholders, including academic groups, that is intended to promote mutual learning and discussion. Specifically, FDA is soliciting feedback on the opportunities and challenges with utilizing AI/ML in the development of drugs, as well as in the development of medical devices intended to be used with drugs. This feedback will provide an additional resource to help inform the regulatory landscape in this area. Additionally, it is beneficial for researchers and technology developers, particularly those new to drug development and human subjects research, to recognize some of the initial thinking and considerations involved with utilizing these technologies, including having familiarity with FDA's current activities, initiatives, practices, and potentially applicable regulations.
Discussion paper: https://www.fda.gov/science-research/science-and-research-special-topics/artificial-intelligence-and-machine-learning-aiml-drug-development