Findings
The document introduces a risk-based credibility assessment framework for establishing and evaluating the credibility of an Artificial Intelligence (AI) model's output when used to support regulatory decisions regarding drug safety, effectiveness, or quality. The framework outlines a 7-step process beginning with defining the question of interest and the Context of Use (COU). Credibility is defined as trust, established through evidence, in the AI model's performance for a particular COU. The credibility assessment is tailored to the AI model risk, which is a combination of model influence (the AI model's evidence contribution relative to other evidence) and decision consequence (the significance of an adverse outcome from an incorrect decision). The document highlights challenges with AI use, including variability in development datasets (training/tuning), the need for methodological transparency due to model complexity, difficulty in quantifying and interpreting uncertainty in model output, and the potential for performance change over time (data drift), which necessitates life cycle maintenance.

Recommendations
Sponsors and interested parties should define the question of interest and clearly define the COU, detailing the AI model's specific role and scope and whether other information will be used. They should assess the AI model risk (low, medium, or high) to ensure that subsequent credibility assessment activities (Step 4) are commensurate with that risk and tailored to the COU. For Step 4, the credibility assessment plan should include a description of the model, model development process (including inputs, architecture, feature selection, and rationale), and data used (training and tuning data). Development data must be deemed fit for use (relevant and reliable) to mitigate issues like algorithmic bias. The plan should also detail the model evaluation process using independent test data and include performance metrics with confidence intervals, an estimate of uncertainty, and a description of model limitations. Early engagement with the FDA is strongly encouraged to discuss model risk and the adequacy of the credibility assessment plan.

Regulatory Considerations
The risk-based credibility assessment framework is intended to help organize and document information for regulatory submissions. The required stringency of assessment activities and the level of documentation should be commensurate with the AI model risk. For AI models whose performance can change over time (e.g., in pharmaceutical manufacturing or postmarketing), sponsors must implement life cycle maintenance plans to monitor performance and manage changes in a risk-based manner. Changes to AI models should be evaluated through the manufacturer's change management system and may require re-execution of parts of the credibility assessment plan. Early engagement can be facilitated through formal meetings (e.g., Pre-IND) or other specialized programs listed in the guidance, such as the Center for Clinical Trial Innovation (C3TI), the Model-Informed Drug Development (MIDD) Paired Meeting Program, and the Emerging Technology Program (ETP) or Advanced Technologies Team (CATT).