Let’s talk about something exciting – and sometimes a little challenging: the world of artificial intelligence (AI) in medical devices. As an engineer specializing in UX & UI, I am convinced that great products require both an understanding of technology and a deep knowledge of usage and users.
When AI is applied to a medical device, it quickly becomes interesting. Both from a technology perspective and from a user perspective. In addition, there are a growing number of guidelines (many still in draft form) that govern the use of AI in medical devices.
Reason enough to take a closer look at these guidelines. In this series we ask the question: What are the implications for usability and UI design? Each part of the series will examine one guideline in detail.
In this overview, we present the guidelines that we have already looked at in detail. Think of it as a summary and table of contents. Since our series isn’t complete, it’s worth checking back periodically to see if any parts have changed or new ones have been added. Alternatively, you can stay up to date by subscribing to our newsletter.
Let’s get started!
Part 1: Good Machine Learning Practice for Medical Device Development – Guiding Principles
The first part of the series focuses on the IMDRF’s Good Machine Learning Practice (GMLP) Guiding Principles. The draft presents 10 principles that serve as a starting point for the development of AI-based medical devices. But what do they mean for usability?
The usability of AI-based medical devices must be systematically integrated from the outset, as regulators expect them to fit seamlessly into clinical workflows and be easy to use. In particular, human-AI collaboration, transparency of AI decisions, and multidisciplinary development play a central role. In addition, security aspects, such as error-avoidance authentication processes, must be implemented in a user-friendly manner and clearly communicated.
Part 2: AI-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations
The second part of the series highlights the FDA guidance document “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations.” The draft defines how AI-enabled medical devices must be designed to be safe and transparent throughout their lifecycle.
It shows that Usability is an increasing focus for regulators: users must be able to understand not only the results, but also how they were obtained. Human-AI collaboration, risk analysis and dynamic decision making must be designed to be transparent and user friendly. Clear documentation and continuous monitoring are essential to minimize risks such as misunderstandings or incorrect decisions.
What happens next?
As mentioned in the introduction, this series will be continually updated. On the one hand, we will see if the drafts have evolved. We will also add new guidelines to the series. If you have a specific guideline that you would like to see here, please write to us.
