Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.

翻译：人工智能（AI）模型在医学领域的应用日益广泛，但其决策的可解释性引发关注。本文对可解释人工智能（XAI）进行系统性分析，重点关注当前医疗领域应用的模型。文献检索依据系统综述与元分析优先报告条目（PRISMA）标准，涵盖2012年1月1日至2022年2月2日期间发表的相关研究。本综述分析了XAI领域的主流趋势，并梳理了主要研究方向。我们从“为何”“如何”“何时”三个维度探究这些XAI模型的应用及其影响，系统考察了XAI方法论，并阐释了如何通过描述医疗领域AI模型构建可信AI。本文讨论将有助于推动XAI领域的规范化发展。