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# FDA Needs New Policies for AI: Gottlieb
- URL: https://www.fdaweb.com/fda-needs-new-policies-for-ai-gottlieb/
- Published: 2023-06-09T12:00:00.000Z
- Updated: 2026-09-14T18:38:17.000Z
- Author: David McFarland
- Tags: FDA Policy/General, #legacy-id-D5154663

FDA needs new policies geared toward integrating artificial intelligence (AI) into drug and medical product development, according to former FDA commissioner **Scott Gottlieb** and his former chief of staff **Lauren Silvis**. Writing in the [6/8 online](http://FDA needs new policies geared toward integrating artificial intelligence %28AI%29 into drug and medical product development, according to former FDA commissioner Scott Gottlieb and his former chief of staff Lauren Silvis. Writing in the 6/8 online JAMA Forum, they %28XXXX SUBSTITUTE, DELETE XXX%29 say that integrating AI “technologies into the current regulatory frameworks presents a considerable challenge. Global regulatory bodies, including [FDA], %28XXX INSERT, DELETE XXX%29will grapple with the task of applying their established norms to these novel entities.”     Gottlieb and Silvis note that AI will likely help “transform binary, objective endpoints into statistical metrics of disease severity that are much more predictive; for example, providing a more quantitative measure of heart failure rather than relying on a composite measure from a small number of objective outputs such as left ventricular ejection fraction and exercise tolerance. Artificial intelligence can translate a larger universe of genomic, proteomic, and phenotypic data into a more complex assessment of risk, improving clinical trial design to select patients who are more likely to benefit from a treatment and — using the same tool in clinical practice — refining stratification variables for effective prescribing decisions.”     An AI regulatory pathway may require Congress to grant the agency specific authorities to establish proper policies, the two ex-officials %28XXX SUBSTITUTE XXX%29say. “Contrary to the FDA’s conventional regulatory approach, which is typically retrospective—evaluating the safety and efficacy of products after they are fully developed — AI regulation may require an approach that more closely resembles the new authorities that Congress granted the FDA to regulate over-the-counter drugs,” they point out. “This process involves defining prospective criteria that guide the safe development of products, and then ensuring that new entrants adhere to these established standards.”     Additionally, Gottlieb and Silvis see a risk-based regulatory approach in situations where the output from AI tools may be informative rather than determinative. “For applications incorporated into drug approvals or serving as standalone medical devices, regulators could accept narrow indications with labeling that helps users understand the limitations,” they write. “A suitably tailored framework should also consider the intended influence of AI output on clinical decision-making, incorporating flexibility. This framework would include giving due consideration to circumstances where clinicians apply their own expertise and judgment in conjunction with an AI-generated output.”) *JAMA Forum*, they say that integrating AI “technologies into the current regulatory frameworks presents a considerable challenge. Global regulatory bodies, including \[FDA\], will grapple with the task of applying their established norms to these novel entities.”

Gottlieb and Silvis note that AI will likely help “transform binary, objective endpoints into statistical metrics of disease severity that are much more predictive; for example, providing a more quantitative measure of heart failure rather than relying on a composite measure from a small number of objective outputs such as left ventricular ejection fraction and exercise tolerance. Artificial intelligence can translate a larger universe of genomic, proteomic, and phenotypic data into a more complex assessment of risk, improving clinical trial design to select patients who are more likely to benefit from a treatment and — using the same tool in clinial practice — refining stratification variables for effective prescribing decisions.”

An AI regulatory pathway may require Congress to grant the agency specific authorities to establish proper policies, the two ex-officials say. “Contrary to the FDA’s conventional regulatory approach, which is typically retrospective—evaluating the safety and efficacy of products after they are fully developed — AI regulation may require an approach that more closely resembles the new authorities that [Congress](https://www.congress.gov/bill/116th-congress/house-bill/748/text?ref=fdaweb.com) granted the FDA to regulate [over-the-counter drugs](https://www.gao.gov/products/gao-20-572?ref=fdaweb.com),” they point out. “This process involves defining prospective criteria that guide the safe development of products, and then ensuring that new entrants adhere to these established standards.”

Additionally, Gottlieb and Silvis see a risk-based regulatory approach in situations where the output from AI tools may be informative rather than determinative. “For applications incorporated into drug approvals or serving as standalone medical devices, regulators could accept narrow indications with labeling that helps users understand the limitations,” they write. “A suitably tailored framework should also consider the intended influence of AI output on clinical decision-making, incorporating flexibility. This framework would include giving due consideration to circumstances where clinicians apply their own expertise and judgment in conjunction with an AI-generated output.”