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Why do explainable AI (XAI) explanations in radiology, despite their promiseof transparency, still fail to gain human trust? Current XAI approaches providejustification for predictions, however, these do not meet practitioners' needs.These XAI explanations lack intuitive coverage of the evidentiary basis for agiven classification, posing a significant barrier to adoption. We posit thatXAI explanations that mirror human processes of reasoning and justificationwith evidence may be more useful and trustworthy than traditional visualexplanations like heat maps. Using a radiology case study, we demonstrate howradiology practitioners get other practitioners to see a diagnosticconclusion's validity. Machine-learned classifications lack this evidentiarygrounding and consequently fail to elicit trust and adoption by potentialusers. Insights from this study may generalize to guiding principles forhuman-centered explanation design based on human reasoning and justification ofevidence.

Explainable AI And Visual Reasoning: Insights From Radiology