Two discussion papers offer insight on machine learning
The Food & Drug Administration just released two papers regarding its viewpoint on artificial intelligence and machine learning, and they include concerns.
"Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work," begins a May report written by Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research at the FDA. She follows with a much-needed clarification: "The U.S. Food and Drug Administration uses the term AI to describe 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 a subset of AI that uses data and algorithms, without being explicitly programmed, to imitate how humans learn."
Many of the general public aren't quite sure what exactly "AI" is. Within businesses, EA has encountered everything from skepticism to enthusiam about the technology. In an industrial realm, most folks are more aware of its definition than the general public mentioned above, however, the distinction between "AI" and "machine learning" is often misunderstood. This editor can speak from experience of mistakely conflating the two.
Cavazzoni continues: "As with other evolving fields of science and technology, there are challenges associated with AI/ML in drug development, such as ethical and security considerations like improper data sharing or cybersecurity risks. There are also concerns with using algorithms that have a degree of opacity, or algorithms that may have internal operations that are not visible to users or other interested parties. This can lead to amplification of errors or preexisting biases in the data. We aim to prevent and remedy discrimination — including algorithmic discrimination, which occurs when automated systems favor one category of people over other(s) — to advance equity when using AI/ML techniques. To address these concerns, the FDA has released a discussion paper, “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products."
She notes that the discussion paper is a collaboration among the FDA’s Center for Drug Evaluation and Research, the Center for Biologics Evaluation and Research, and the Center for Devices and Radiological Health, including its Digital Health Center of Excellence.
The paper includes an overview of the current and potential future uses for AI/ML in therapeutic development. It also discusses the possible concerns and risks associated with these innovations and ways to address them. For instance, the paper describes the importance of having human involvement, which will vary depending on how the technologies will be used. The paper also emphasizes adopting a risk-based approach to evaluate and manage AI/ML in facilitating innovations and protecting public health.
To further address the use of AI in drug manufacturing, CDER issued another discussion paper, Artificial Intelligence in Drug Manufacturing, as part of the Framework for Regulatory Advanced Manufacturing Evaluation (FRAME) Initiative. AI technologies are important in drug manufacturing because they can enhance process controls, identify early warning signals, and prevent product losses. We are also planning a second workshop for stakeholders to discuss the questions in our AI in drug manufacturing discussion paper.
