Levels Of Autonomy And Safety Assurance For Ai-Based Clinical Decision Systems

Levels of Autonomy are an important guide to structure our thinking of capability, expectation and safety in autonomous systems. Here we focus on autonomy in the context of digital healthcare, where autonomy maps out differently to e.g. self-driving cars. Specifically we focus here on mapping levels of autonomy to clinical decision support systems and consider how these levels relate to safety assurance. We then explore the differences in the generation of safety evidence that exist between medical applications based on supervised learning (often used for prediction tasks such as in diagnosis and monitoring) and reinforcement learning (which we recently established as a way for AI-guided medical intervention). These latter systems have the potential to intervene on patients and should therefore be regarded as autonomous systems.