The Food and Drug Administration plans to release new guidance on the use of artificial intelligence in pharmaceutical research. The new recommendations, slated before the end of the year, follow a consolidation of three AI working groups into one council that will guide the agency’s AI policy work. The guidance is based in part on responses the agency received to a 2023 discussion paper on the topic. The paper received more than 800 comments from 65 organizations, said Tala Fakhouri, the FDA’s associate director for data science and AI policy. Between 2016 and 2022, the agency reviewed more than 500 drug submissions in which AI was used. Fakhouri spoke with Ruth about how the agency is handling the new technology. The interview has been edited for length and clarity. What are some examples of how companies are using AI in drug development? AI can be used in a clinical trial to stratify patients for different dosages or to identify which patients require inpatient monitoring after they take the drug. There’s also a lot of AI that’s being used to increase the efficiency of drug trials so that you don’t have to recruit a lot of patients to take a placebo. Then there’s a lot of work in advanced manufacturing, identifying safety signals after a drug has been marketed and it’s out in the population. How is the FDA adjusting? Our evidentiary standards are the same. We sometimes get asked these questions when it comes to emerging technologies: “Are FDA standards going to change? Are you potentially going to dilute the regulatory standards?” Absolutely not. But when AI is used, we can’t just review the output. We also need to learn about how you built your model. How did you train it? What data did you use to train it? Is the data fit for the purpose? What are challenges to evaluating AI? We already know that we need to ask questions about the data. A lot of that data sits either in silos within the same organization or across organizations. So the one thing that I think collectively the ecosystem needs to think about is: How do we increase the pool of fit-for-purpose data to train models better?
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