FDA 2025 AI Draft Guidance

In this video, we dive deep into the intersection of Artificial Intelligence, advanced analytics, and regulatory science . The FDA recently released its highly anticipated draft guidance, "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products" . If you are working in clinical development, manufacturing, or postmarketing pharmacovigilance, understanding this regulatory roadmap is essential . 🔍 Key Topics We Cover in This Episode: The 7-Step Credibility Assessment Framework: How to establish and evaluate an AI model's trustworthiness for a specific Context of Use (COU) . Decoding "Model Risk": Understanding how the FDA evaluates risk based on Model Influence (how much the AI contributes to the decision) and Decision Consequence (the severity of a wrong decision) . "Fit for Use" Data: Why training and testing data must be both relevant and reliable to prevent algorithmic bias and ensure real-world accuracy . Life Cycle Maintenance & Data Drift: Why AI models—especially self-evolving ones—require continuous, risk-based monitoring to maintain credibility over the drug product life cycle . Early Engagement: The pathways available to sponsors for engaging with the FDA early on AI model development . As we continue to push the boundaries of causal methods and advanced analytics in Real-World Evidence (RWE), aligning our operations with these new regulatory expectations is the key to mastery . 🗣️ We want to hear from you! How is your organization preparing to integrate AI into your evidence-generation workflows? Let us know in the comments below! 👍 Don't forget to LIKE, SUBSCRIBE, and hit the notification bell so you never miss an update on the tools and tech shaping the future of real-world evidence! (Note: The FDA guidance discussed in this video is a draft document distributed for comment purposes.)