JMIR Publications

ABSTRACT

One of the greatest strengths of artificial intelligence and machine learning (AI/ML) approaches in healthcare is that their performance can be continually improved based on updates from automated learning from data. However, healthcare AI/ML models are currently essentially regulated under provisions that were developed for an earlier age of slowly updated medical devices - requiring major documentation reshape and re-validation with every major update of the model generated by the ML algorithm. This creates minor problems for models that will be re-trained and updated only occasionally, but major problems for models that will learn from data in real-time or near real-time. Regulators have announced action plans for fundamental changes in regulatory approaches. Here, we examine the current regulatory frameworks and the developments in this domain. The status quo and recent developments are reviewed, and we argue that these innovative approaches to healthcare need these matching innovative approaches to regulation and that these approaches will bring benefits for patients. International perspectives from the WHO, and the FDA’s proposed approach, based around oversight of tool developers’ quality management systems and defined algorithm change protocols, offer a much-needed paradigm shift, and strive for a balanced approach to enabling rapid improvements in healthcare through AI innovation, whilst simultaneously ensuring patient safety. The draft EU regulatory framework indicates similar approaches, but no detail has yet been provided on how algorithm change protocols will be implemented in the EU, and this is required for the full benefits of AI/ML-based innovation for patients and for EU healthcare systems to be realised.