Proving AI in Medical Devices
From Prototype to Approval
Most medical device teams can get an AI model to work. Proving that it works, safely and predictably, is where projects stall. And the bar is rising: clients now want to know where AI was used, which tools were involved, and how the outputs were reviewed and verified.
This white paper sets out what teams need to settle before the build starts, why the proof must be planned from week one, and how to get from a working prototype to an approved product without a rebuild.
What you’ll learn:
Why “AI in medical devices” means two different things, AI that builds the software and AI that runs inside the device, and why the checks for one don’t transfer to the other
A triage for where AI belongs in a safety-critical design, and the three signs that it doesn’t
Why AI components fail quietly, and why an accuracy figure says almost nothing about safety
Four moves that prove the system rather than the model
The four pieces of evidence a team should be able to produce at any point in a project, not reconstruct at the end
The three kinds of knowledge that need to be in the room from week one, and three questions to take into your next design review
Want to hear more? Watch our webinar, AI in Medical Devices: Making It Work, and Proving It, featuring experts from Critical Software, IQ Inc., and Bausch + Lomb, as they discuss the real-world challenges of bringing AI-enabled medical devices from prototype to market.
Watch the full webinar on YouTube: https://www.youtube.com/watch?v=ldl7PGO9RNc