可解释AI与视觉推理：来自放射学的见解
Explainable AI And Visual Reasoning: Insights From Radiology
AI总结：
本文探讨放射学中XAI解释为何无法获得信任，提出镜像人类推理与证据论证过程的解释设计原则，以提升其实用性与可信度。
摘要：
为什么放射学中的可解释AI（XAI）解释，尽管承诺了透明度，却仍无法获得人类信任？当前的XAI方法为预测提供了理由，然而这些方法并未满足从业者的需求。这些XAI解释缺乏对给定分类证据基础的直观覆盖，构成了采用的重大障碍。我们假设，镜像人类推理和证据论证过程的XAI解释，可能比热图等传统视觉解释更有用且更值得信赖。通过一个放射学案例研究，我们展示了放射学从业者如何让其他从业者看到诊断结论的有效性。机器学习的分类缺乏这种证据基础，因此未能激发潜在用户的信任和采用。本研究的见解可推广为基于人类推理和证据论证的以人为中心的解释设计指导原则。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
