  Generative artificial intelligence (GenAI) has the potential to improve
healthcare through automation that enhances the quality and safety of patient
care. Powered by foundation models that have been pretrained and can generate
complex content, GenAI represents a paradigm shift away from the more
traditional focus on task-specific classifiers that have dominated the AI
landscape thus far. We posit that the imminent application of GenAI in
healthcare will be through well-defined, low risk, high value, and narrow
applications that automate healthcare workflows at the point of care using
smaller foundation models. These models will be finetuned for different
capabilities and application specific scenarios and will have the ability to
provide medical explanations, reference evidence within a retrieval augmented
framework and utilizing external tools. We contrast this with a general,
all-purpose AI model for end-to-end clinical decision making that improves
clinician performance, including safety-critical diagnostic tasks, which will
require greater research prior to implementation. We consider areas where
'human in the loop' Generative AI can improve healthcare quality and safety by
automating mundane tasks. Using the principles of implementation science will
be critical for integrating 'end to end' GenAI systems that will be accepted by
healthcare teams.
