A closer look at the FDA’s draft guidance on AI-driven drug data

Artificial intelligence (AI) is rapidly transforming the landscape of drug development, offering innovative solutions to complex challenges in the pharmaceutical industry. Recognizing the profound impact of AI, the U.S. Food and Drug Administration (FDA) has issued a draft guidance titled "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products."

This document provides a framework for applying AI in drug development, emphasizing a risk-based credibility assessment to ensure the safety, effectiveness, and quality of drugs and biological products.

The role of AI in drug development

AI is increasingly utilized across various stages of the drug product life cycle, including nonclinical, clinical, postmarketing, and manufacturing phases. Its applications range from predicting patient outcomes and understanding disease progression to processing and analyzing large datasets, such as real-world data and information from digital health technologies. The FDA has observed a significant increase in drug application submissions incorporating AI components, reflecting the technology's growing importance in the pharmaceutical sector.

Purpose of the draft guidance

The primary objective of the FDA's draft guidance is to provide recommendations to sponsors and other stakeholders on using AI to generate information or data that supports regulatory decision-making concerning drug safety, effectiveness, or quality. The guidance introduces a risk-based credibility assessment framework designed to establish and evaluate the reliability of an AI model within a specific context of use (COU). This approach aims to build trust in AI models by collecting evidence regarding their performance for particular applications.

Key components of the risk-based credibility assessment framework

The FDA outlines a seven-step process in its risk-based credibility assessment framework:

1. Define the question of interest: Clearly articulate the specific question, decision, or concern the AI model aims to address.
2. Define the context of use (COU): Describe in detail what the model will analyze and how its outputs will be utilized. This includes specifying whether the AI model's output will be used alongside other information or as the sole basis for decision-making.
3. Assess model influence risk: Evaluate the extent to which the AI model influences decision-making processes. This involves determining the degree of reliance placed on the model's outputs.
4. Assess decision consequence risk: Consider the potential impact of decisions informed by the AI model, particularly concerning patient safety and drug quality.
5. Determine credibility requirements: Based on the assessed risks, establish the necessary level of credibility for the AI model. Higher risks necessitate more stringent credibility requirements.
6. Develop a credibility plan: Formulate a plan detailing the activities and evidence needed to demonstrate the AI model's credibility within its COU.
7. Execute the credibility plan and document results: Implement the plan, gather evidence, and document findings to support the AI model's credibility.

Importance of early engagement with the FDA

The FDA strongly encourages sponsors and other interested parties to engage early with the agency to discuss specific AI model development and usage plans. Early dialogue can help clarify expectations, address potential concerns, and ensure that the AI model aligns with regulatory standards. This proactive approach facilitates a smoother evaluation process and promotes the responsible integration of AI in drug development.

Public participation and feedback

The FDA is seeking public comments on the draft guidance to ensure it aligns with industry experiences and adequately addresses stakeholder needs. The agency is particularly interested in feedback on the applicability of the risk-based credibility assessment framework and the sufficiency of engagement options for sponsors and other parties. Comments can be submitted online or via mail, with a deadline set to ensure timely consideration before finalizing the guidance.

Conclusion

The FDA's draft guidance represents a significant step toward integrating AI into the drug development process. By establishing a clear framework for assessing AI model credibility, the FDA aims to balance fostering innovation and ensuring public safety. Stakeholders are encouraged to review the draft guidance, participate in the public comment process, and engage with the FDA early in their AI model development endeavors. This collaborative approach will help shape a regulatory environment that supports the safe and effective use of AI in advancing medical product development.

References

FDA. (2025, January 6). Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological

FDA. (2025, January 6). FDA Proposes Framework to Advance Credibility of AI Models Used for Drug and Biological Product Submissions. Retrieved from https://www.fda.gov/news-events/press-announcements/fda-proposes-framework-advance-credibility-ai-models-used-drug-and-biological-product-submissions

FDA. (2025, January). Artificial Intelligence for Drug Development. Retrieved from https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development

DLA Piper. (2025, January 23). Key takeaways from FDA’s draft guidance on the use of AI in drug and biological life cycle. Retrieved from https://www.dlapiper.com/en-us/insights/publications/2025/01/fda-releases-draft-guidance-on-use-of-ai

Foley & Lardner LLP. (2025, January). AI Drug Development: FDA Releases Draft Guidance. Retrieved from https://www.foley.com/insights/publications/2025/01/ai-drug-development-fda-releases-draft-guidance/

Hogan Lovells. (2025, January). FDA unveils long-awaited guidance on AI use to support drug and biologic development. Retrieved from https://www.hoganlovells.com/en/publications/fda-unveils-longawaited-guidance-on-ai-use-to-support-drug-and-biologic-development

[RAPS. (2025, January). AI in drug development: FDA draft guidance addresses credibility assessments. Retrieved from https://www.raps.org/news-and-articles/news-articles/2025/1/ai-in-drug-development-fda-draft-guidance-addresse