Since the early days of the Explainable AI movement, post-hoc explanations have been praised for their potential to improve user understanding, promote trust, and reduce patient safety risks in black box medical AI systems. Recently, however, critics have argued that the benefits of post-hoc explanations are greatly exaggerated since they merely approximate, rather than replicate, the actual reasoning processes that black box systems take to arrive at their outputs. In this article, we aim to defend the value of post-hoc explanations against this recent critique. We argue that even if post-hoc explanations do not replicate the exact reasoning processes of black box systems, they can still improve users' functional understanding of black box systems, increase the accuracy of clinician-AI teams, and assist clinicians in justifying their AI-informed decisions. While post-hoc explanations are not a "silver bullet" solution to the black box problem in medical AI, we conclude that they remain a useful strategy for addressing the black box problem in medical AI.

本研究探讨了医疗人工智能中事后解释的价值，针对批评者认为其效果夸大的观点进行了辩护。尽管事后解释并不能完全复制黑箱系统的推理过程，但仍能改善用户对系统的理解，提升临床团队的准确性，帮助医生更好地解释其基于人工智能的决策。因此，事后解释在解决医疗人工智能的黑箱问题中仍然具有重要价值。

为医疗人工智能中的事后解释辩护