IEEE Trans Neural Netw Learn Syst. 2021 Nov;32(11):4793-4813. doi: 10.1109/TNNLS.2020.3027314. Epub 2021 Oct 27.
Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.
最近，人工智能和机器学习在许多任务中表现出色，从图像处理到自然语言处理，特别是随着深度学习（DL）的出现。随着研究的进展，它们已经涉足许多不同的领域和学科。其中一些领域需要高度的问责制和透明度，例如医疗领域。因此，需要对机器决策和预测进行解释，以证明其可靠性。这需要更高的可解释性，这通常意味着我们需要了解算法背后的机制。不幸的是，DL 的黑盒性质仍然没有得到解决，许多机器决策仍然难以理解。我们提供了不同研究工作提出的可解释性的综述，并对其进行了分类。不同的类别在可解释性研究中展示了不同的维度，从提供“明显”可解释信息的方法到复杂模式的研究。通过将相同的分类应用于医学研究中的可解释性，希望：1）临床医生和从业者随后可以谨慎地采用这些方法；2）通过更多地考虑医疗实践，对可解释性有更深入的了解；3）鼓励推动基于数据、基于数学和基于技术的医学教育的举措。
