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US Pharma and Biotech Summit 2024: Artificial Intelligence and Machine Learning Through the Eyes of the FDA Part II
In an interview with Pharm Exec Associate Editor Don Tracy, Tala Fakhouri, at Financial Times’ US Pharma and Biotech Summit, Tala Fakhouri, Associate Director for Policy Analysis, FDA, discusses whether the FDA plans on restricting the use of AI and what the future could look like when it comes to machine learning.
PE: Do you see the FDA placing any restrictions on the use of AI and machine learning as times goes on? What may prompt such actions?
Fakhouri: Like I mentioned during the keynote interview, we get asked, does FDA regulate large language models? Are you going to ban generative AI use? My response is that we typically don't regulate linear regression. We look at the data and the information that any modeling technique is producing, and we want to make sure that the information is trustworthy. So, I wouldn't say that we would be banning or prohibiting a certain AI or machine learning type of algorithm, what we're actually interested in is how robust how accurate, how credible, the information from these models is.
PE: What do you think the future may hold for AI and machine learning in pharma R&D in both the short- and long-term?
Fakhouri: We're actually very excited about AI use, I think we're seeing that it's increasing efficiencies in different parts of the drug development process. If you think about things such as discovery or protein folding, which again, is outside of what we normally look at, it could potentially cut the development time by years. This is all very exciting, because it could translate into faster, safe and effective drugs coming into the market. It can also fill in certain gaps for rare diseases, for example, where we can see a lot of potential use for AI to accelerate the development of drugs. In this type of situation, that's what I would say would be the long term. With the short term, I think what we're all doing, whether it's industry, whether it's the regulator's academia, is we're going through this adoption curve. You need to train your staff, you need to bring in the right expertise, and you need to develop the right tools to solve the right problems. That's going to take some time and that's why I think the short term uses of AI are going to be mostly low hanging type of fruits where you're increasing operational efficiency, but then that will translate into the development of safe and effective drugs faster.
