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

翻译：为何放射学中的可解释人工智能（XAI）解释尽管承诺透明性，却仍未能赢得人类的信任？当前的XAI方法提供预测的合理性证明，但这些证明未能满足从业者的需求。这些XAI解释缺乏对给定分类证据基础的直观覆盖，构成了其被采纳的重大障碍。我们认为，模仿人类推理与证据论证过程的XAI解释，可能比热力图等传统视觉解释更具实用性和可信度。通过放射学案例研究，我们展示了放射学从业者如何让其他从业者认可诊断结论的有效性。机器学习分类缺乏这种证据基础，因此无法引发潜在用户的信任和采纳。本研究的见解或可推广至基于人类推理与证据论证的人本解释设计指导原则。