即使不还原黑箱逻辑，事后解释仍能提升医生对AI的理解与决策准确率。
In defence of post-hoc explanations in medical AI
- 用事后解释帮助医生理解AI决策逻辑，而非要求完全复现其内部过程。
- 实验证明可提升医-机协作团队的诊断准确率。
- 适合临床场景中需要快速可信解释的医疗AI应用。
自可解释AI兴起以来，事后解释因其有望提升用户理解、增强信任并降低患者安全风险，被广泛视为改善黑箱医疗AI系统的重要手段。然而，近期批评者认为其价值被夸大——因为事后解释仅近似而非复现黑箱系统的实际推理过程。本文旨在回应这一质疑：即便无法完全还原内部机制，事后解释仍能增强使用者对黑箱系统的功能性理解，提升医-机团队决策准确性，并辅助医生为基于AI的决策提供依据。尽管事后解释并非解决黑箱问题的万能方案，但仍是当前医疗AI中应对透明度挑战的有效策略。
原文摘要 · Abstract (English)
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.
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