The power and promise of artificial intelligence comes in a Pandora’s box full of novel and multi-dimensional enterprise risks legal, regulatory, financial, operational, ethical and reputational that go above and beyond those associated with other technology innovations.
In our last post, we took a brief look back through history at FDA's approach to regulating medical device software and found that there is little distinction from the agency's approach to hardware devices.
FDA announced several digital health initiatives aimed at improving the agency’s resources and policies governing software and data systems. We will review FDA’s digital health improvement highlights and take a quick look at AI/ML-enabled medical devices.
In our last post, we took a brief look back through history at FDA’s approach to regulating medical device software and found that there is little distinction from the agency’s.
To embed, copy and paste the code into your website or blog: The gathering and transmitting of personal data represents a major cyber threat to medical devices and must be extremely carefully thought through.
Q: The FDA’s stance on a regulatory framework for artificial intelligence and machine learning (AI/ML) software as a medical device is continuously evolving. Could you explain the history?
A: Artificial intelligence (AI) is “adaptive,” meaning that it continuously learns algorithms. For this reason, it is sometimes referred to as Machine Learning (ML). Newly designed medical devices that incorporate AI/ML by definition do not have a final “locked” design capable of a single FDA review. In April 2019, the FDA issued a white paper,