Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis.

人工智能是一个广泛的领域，包含了广泛的技术，其中深度学习是目前影响最大的领域。此外，医疗领域的数据既复杂又海量，医生决策的重要性使其成为深度学习技术可以产生最大影响的领域之一。由医生、研究方法学家和计算机科学家组成的多学科团队按照 Cochrane 的建议进行了系统评价。本次调查旨在确定主要的治疗领域以及用于诊断和治疗任务的深度学习模型。最相关的数据库包括 MedLine、Embase、Cochrane Central、Astrophysics Data System、Europe PubMed Central、Web of Science 和 Science Direct。在第一次和第二次同行评审筛选中定义并应用了纳入和排除标准。制定了一套质量标准来选择第二次筛选后获得的论文。最后，从最初的 3493 篇论文中选择了 126 项研究，并对 64 篇进行了描述。结果表明，关于医学深度学习的出版物数量每年都在增加。此外，卷积神经网络是使用最广泛的模型，最发达的领域是肿瘤学，它们主要用于图像分析。结果表明，关于医学深度学习的出版物数量每年都在增加。此外，卷积神经网络是使用最广泛的模型，最发达的领域是肿瘤学，它们主要用于图像分析。结果表明，关于医学深度学习的出版物数量每年都在增加。此外，卷积神经网络是使用最广泛的模型，最发达的领域是肿瘤学，它们主要用于图像分析。