Yujan Shrestha

Setting acceptance criteria incorrectly (too high or too low) can delay your FDA submission by weeks or trigger rejection. This guide analyzed 784 AI/ML medical device clearances to provide concrete, data-driven thresholds. You'll find specific starting points: AUC of 0.9, Sensitivity/Specificity of 0.8, Dice of 0.8, Kappa of 0.85, and Bland-Altman LOA within ±10% of mean physiological values. Learn which statistical methods FDA accepts most often, how to justify your criteria using special controls and predicates, and what to do when your device fails testing.

This article shows how to achieve an AI/ML SaMD 510(k) submission in ~3 months by running a compact, well-powered MRMC study while continuously accruing standalone evidence through washout. It clarifies roles, sizing, units, and endpoints, with device examples, flowcharts, and a checklist to prevent rework and delays.

Here we analyze over 200 FDA 510(k) and De Novo summaries to tease out best practices for AI/ML study designs and adjudication strategies specifically.

A practical guide for engineers new to medical imaging AI, highlighting common pitfalls—like coordinate-system errors and orientation mix-ups—and providing simple checks to avoid them.Ask ChatGPT

Ideas on how to get foundation models and generative AI FDA cleared.

Recent advancements in radiology have led to the development of foundation models integrating vision and language capabilities, transforming medical imaging analysis

This article dissects a real AI/ML SaMD 510(k) submission (Body Check CT Cardiomegaly, K232613) with downloadable examples. After wasting a year over-engineering my first submission, I’ll share practical insights to prevent this from happening to you!

Command Line as a Medical Device (CLaMD) represents a streamlined approach to FDA clearance for medical software. By utilizing command-line interfaces instead of graphical user interfaces, developers can significantly simplify the regulatory process. This approach reduces UI hazard analysis requirements and minimizes human factors testing since interaction occurs through established systems like PACS or EHR.
CLaMD is particularly effective for AI algorithms, which can run automatically, output standardized formats, and operate server-side. This allows clinicians to continue using their familiar workstations while the AI works invisibly in the background. Numerous FDA-cleared devices have successfully implemented this pattern, demonstrating its viability in medical device submissions.

A panel discussion about the FDA's Predetermined Change Control Plan (PCCP) guidance, particularly its application to AI-enabled medical devices. The conversation includes panelists sharing insights into regulatory processes, real-world examples, and strategies to implement PCCP effectively. The panelists also addressed audience questions about PCCP's implications for reducing submissions and streamlining changes. The discussion concludes with remarks on how PCCP can benefit the medical device industry in terms of compliance, cost reduction, and innovation.

Discussion covered key takeaways from the RSNA conference, balancing safety with speed, and setting risk thresholds. Topics included statistical power, U.S. data needs, AI/ML sample sizing, and using the PCCP tool to de-risk regulatory pathways. Collaboration between regulatory and engineering teams was emphasized for managing software changes.