Regulating radiology AI medical devices that evolve in their lifecycle

TL;DR

This paper discusses recent regulatory changes in the US and EU that facilitate the safe updating of evolving radiology AI devices, emphasizing the importance of data management and quality monitoring.

Contribution

It provides an overview of new regulations and outlines key components needed for deploying dynamic AI systems in radiology.

Findings

European AI Act and FDA PCCP streamline model updates

Regulations require clear data collection and re-training descriptions

Emphasis on real-world quality monitoring mechanisms

Abstract

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image characteristics. Consequently, manufacturers must conduct regular updates to ensure that they remain safe and effective. Performing such updates in the United States and European Union required, until recently, obtaining re-approval. Given the time and financial burdens associated with these processes, updates were infrequent, and obsolete systems remained in operation for too long. During 2024, several regulatory developments promised to streamline the safe rollout of model updates: The European Artificial Intelligence Act came into effect last August, and the Food and Drug Administration (FDA) issued final marketing submission recommendations for a Predetermined Change Control Plan (PCCP) in December. We provide an overview of these developments and outline the key building blocks necessary for successfully deploying dynamic systems. At the heart of these regulations - and as prerequisites for manufacturers to conduct model updates without re-approval - are clear descriptions of data collection and re-training processes, coupled with robust real-world quality monitoring mechanisms.