Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artificial intelligence, also known as XAI, which targets to explain the results of such black box models to permit the desired assessment. This survey analyses several recent studies in the XAI field applied to medical diagnosis research, allowing some explainability of the machine learning results in several different diseases, such as cancers and COVID-19.

翻译：近几年来，关于深度学习在医学领域应用的研究数量急剧增加。为了向所有参与医学检查的人员解释这些模型的结果，需要对其进行严格的评估。机器学习领域的一个新兴方向是可解释人工智能（XAI），其目标是对此类黑箱模型的结果进行解释，以实现所需的评估。本综述分析了近年来XAI领域应用于医学诊断研究的若干研究，这些研究使得机器学习在多种不同疾病（如癌症和COVID-19）的诊断结果具备了一定的可解释性。