7 papers
The promise and perils of AI in medicine
Robert Sparrow, Joshua Hatherley
What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey,…
In defence of post-hoc explanations in medical AI
Joshua Hatherley, Lauritz Munch, Jens Christian Bjerring
Since the early days of the Explainable AI movement, post-hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient sa…
High hopes for "Deep Medicine"? AI, economics, and the future of care
Robert Sparrow, Joshua Hatherley
In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practi…
Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges
Joshua Hatherley, Robert Sparrow
Objectives: Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data ov…
A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity
Joshua Hatherley
Machine learning (ML) systems are vulnerable to performance decline over time due to dataset shift. To address this problem, experts often suggest that ML systems should be regular…
Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?
Joshua Hatherley
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a m…