AIML FDA Submissions common pitfalls, hard lessons, and faster paths forward

AI/ML submissions can move fast — until they hit the FDA questions that slow everything down: unclear intended use, weak clinical endpoints, shaky ground truth, poor subgroup performance, and change-control plans that don’t match how the model will actually evolve. In this Friday In-Focus session, Leon Doorn (Founder, MedQAIR; Regulatory Specialist and AI System Regulatory Expert) breaks down the most common pitfalls he sees in AI/ML FDA submissions, the hard lessons teams learn the slow way, and the practical steps that create a faster, more defensible path forward. You will learn: • The FDA pathways that matter for AI/ML devices (510(k), De Novo, PMA) and what changes in each • Clinical endpoints and validation expectations that repeatedly trigger follow-up questions • How to define and defend ground truth, labeling, and intended use without over-claiming • How to handle subgroup performance, generalizability, and bias in a reviewable way • What PCCP and change control mean in practice (and what reviewers actually want to see) • How to connect safety risk, cyber risk, and post-market monitoring to your submission story Speaker: • Leon Doorn — Founder, MedQAIR; Regulatory Specialist and AI System Regulatory Expert Replay and chapters below. 00:00 – Welcome and session goals: why AI/ML submissions stall 03:55 – Common pitfalls that trigger FDA questions (and how to avoid them) 08:24 – Choosing the right pathway: De Novo versus 510(k) versus PMA 10:10 – 510(k) expectations for AI/ML: evidence planning and structure 11:48 – Clinical endpoints that matter and why reviewers push back 13:26 – Validation fundamentals: what “good” evidence looks like 15:31 – PCCP and change control: what you must define up front 22:33 – Risk framing: safety risk, performance risk, and regulatory defensibility 36:32 – Ground truth and data quality: the hidden failure point 41:13 – Generalizability and subgroups: proving performance across populations 47:06 – Cybersecurity and connected risk considerations for AI/ML devices 56:48 – Q&A and closing takeaways