Machine learning models are increasingly being used in clinical settings for diagnostic and treatment recommendations, across a variety of diseases and diagnostic methods. To conceptualise how physicians can use them responsibly, and what the standard of care should be, there needs to be discussion beyond model accuracy levels and the types of explanation provided by such classifiers. There needs to be consideration of how the explanations are provided and how historical accuracy rates can together constitute the overall epistemic status of the model, and how models with different epistemic statuses should subsequently be deferred to by medical practitioners. Answering this will require a multi-disciplinary consideration of the literature on automation bias in human factors and ergonomics to higher-order evidence in social epistemology. Adjudicating physician responsibility will also require assessing when a physician's ignorance of the appropriate ways to engage with such classifiers, as outlined above, will be culpable and when not.
Keywords: artificial intelligence; clinical standard of care; machine learning classifiers; medical practitioner responsibility.