The guidance aims to provide clarity on how sponsors, manufacturers, and pharmaceutical industry developers should approach AI to ensure the safe and effective development and commercialization of AI-based tools.

It outlines the role of AI models in the drug product life cycle, including nonclinical, clinical, post-marketing, and manufacturing phases, specifically where AI is used to generate data or information supporting regulatory decisions on safety, efficacy, or product quality. However, it does not address AI applications in drug discovery or operational efficiencies unrelated to patient safety, drug quality, or study reliability.

FDA Commissioner Robert M Califf, MD, notes, ‘With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.’

A key focus of the guidance is ensuring AI model credibility, meaning trust in an AI model’s performance for a given context of use (COU), i.e. the specific role and scope of the AI model in addressing a regulatory question. To support this, the FDA have created a seven-step, risk-based credibility assessment framework: 

1. Define the question the AI model will address.
2. Establish the AI model’s COU.
3. Assess the risk level of the AI model.
4. Develop a plan to ensure credibility within the COU.
5. Execute the credibility assessment plan.
6. Document assessment results and any deviations from the plan.
7. Determine the adequacy of the AI model for the COU.

The draft guidance outlines that the FDA expects sponsors to develop credibility assessment plans that align with the risk level and COU of their AI models. 

The guidance encourages early and proactive engagement between sponsors and the FDA, emphasizing that early discussions can help set clear expectations for AI model credibility assessments, identify potential challenges, and facilitate solutions to regulatory hurdles.

The FDA is seeking public comments on this draft guidance until 7 April 2025. Specifically, feedback is requested on:

• Alignment of the draft guidance with industry practices.
• Adequacy of engagement options for sponsors.
• The need for additional guidance on AI use in post-marketing pharmacovigilance.

If finalized, this would be the first comprehensive FDA guidance outlining the design, development, documentation, and maintenance of AI models used in regulatory decision-making.

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Reference
1. Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products
https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological

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