Trustworthy medical AI systems need to know when they don’t know

Abstract

There is much to learn from Duran and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence (AI). In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defensive medicine or paternalism2—the issue that lies at the heart is an epistemic problem: how can physicians know whether to trust decisions made by AI systems? In this manner, various studies examining the interaction of AI systems and physicians have shown that without being able to evaluate their trustworthiness, especially novice physicians become over-reliant on algorithmic support—and ultimately are led astray by incorrect decisions.3–5 This leads to a second insight from the paper, namely that even if some (deep learning-based) AI system happens to be opaque, it is still not built on the moon. To assess its trustworthiness, AI developers or physicians have different sorts of higher order evidence at hand. Most importantly, …

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