Do We Expect More from Radiology AI than from Radiologists?

TL;DR

This paper compares expectations of AI and human radiologists, highlighting that mistrust in AI decision processes contrasts with greater trust in human decision-making, despite similar complexities and opacities.

Contribution

It critically examines the differing expectations of AI and radiologists, emphasizing the need for balanced trust based on decision process transparency.

Findings

AI decisions are less understood and trusted due to opacity.

Humans are trusted despite biases and poor explainability.

Expectations should align for optimal radiologic interpretation.

Abstract

What we expect from radiology AI algorithms will shape the selection and implementation of AI in the radiologic practice. In this paper I consider prevailing expectations of AI and compare them to expectations that we have of human readers. I observe that the expectations from AI and radiologists are fundamentally different. The expectations of AI are based on a strong and justified mistrust about the way that AI makes decisions. Because AI decisions are not well understood, it is difficult to know how the algorithms will behave in new, unexpected situations. However, this mistrust is not mirrored in our expectations of human readers. Despite well-proven idiosyncrasies and biases in human decision making, we take comfort from the assumption that others make decisions in a way as we do, and we trust our own decision making. Despite poor ability to explain decision making processes in humans, we accept explanations of decisions given by other humans. Because the goal of radiology is the most accurate radiologic interpretation, our expectations of radiologists and AI should be similar, and both should reflect a healthy mistrust of complicated and partially opaque decision processes undergoing in computer algorithms and human brains. This is generally not the case now.