While the pace of development of AI has rapidly progressed in recent years, the implementation of safe and effective regulatory frameworks has lagged behind. In particular, the adaptive nature of AI models presents unique challenges to regulators as updating a model can improve its performance but also introduce safety risks. In the US, the Food and Drug Administration (FDA) has been a forerunner in regulating and approving hundreds of AI medical devices. To better understand how AI is updated and its regulatory considerations, we systematically analyze the frequency and nature of updates in FDA-approved AI medical devices. We find that less than 2% of all devices report having been updated by being re-trained on new data. Meanwhile, nearly a quarter of devices report updates in the form of new functionality and marketing claims. As an illustrative case study, we analyze pneumothorax detection models and find that while model performance can degrade by as much as 0.18 AUC when evaluated on new sites, re-training on site-specific data can mitigate this performance drop, recovering up to 0.23 AUC. However, we also observed significant degradation on the original site after re-training using data from new sites, providing insight from one example that challenges the current one-model-fits-all approach to regulatory approvals. Our analysis provides an in-depth look at the current state of FDA-approved AI device updates and insights for future regulatory policies toward model updating and adaptive AI.

Part of the FDA's action plan includes support for the development of machine learning best practices to evaluate and improve ML algorithms for topics such as data management, interpretability and documentation, as well as advancing real-world performance monitoring pilots. The FDA also noted that the action plan would continue to evolve to stay current with developments in the field of AI/ML-based software as a medical device (SaMD). As the agency pointed out in an April 2019 discussion paper, the potential power of AI/ML-based SaMD lies within its ability to continuously learn, where the adaptation or change to the algorithm is realized after the SaMD is distributed for use and has learned from real-world experience. READ MORE: AI can increase efficiency in healthcare, even in a pandemic. In turn, the autonomous and adaptive nature of these tools requires a new, total product lifecycle regulatory approach that supports a rapid cycle of product improvement, allowing SaMD to continually improve.

US health regulators have approved the first ever artificially intelligent medical device that can identify disease without need for a doctor. The device, called IDx-DR, is designed to detect the most common cause of vision loss among more than 30 million Americans living with diabetes. Its in-built camera takes a picture of the patient's eye, which is assessed by an algorithm to determine whether there are signs of diabetic retinopathy. The move, announced on Wednesday, makes this the first AI device to receive FDA approval to screen without need for a doctor to interpret the results. It means any doctor could use it, including primary care physicians who interact far more frequently with patients with diabetes, rather than patients having to seek out eye doctors themselves.