Atrial Fibrillation (AF) is a type of arrhythmia characterized by irregular heartbeats, with four types, two of which are complicated to diagnose using standard techniques such as Electrocardiogram (ECG). However, and because smart wearables are increasingly a piece of commodity equipment, there are several ways of detecting and predicting AF episodes using only an ECG exam, allowing physicians easier diagnosis. By searching several databases, this study presents a review of the articles published in the last ten years, focusing on those who reported studies using Artificial Intelligence (AI) for prediction of AF. The results show that only twelve studies were selected for this systematic review, where three of them applied deep learning techniques (25%), six of them used machine learning methods (50%) and three others focused on applying general artificial intelligence models (25%). To conclude, this study revealed that the prediction of AF is yet an under-developed field in the context of AI, and deep learning techniques are increasing the accuracy, but these are not as frequently applied as it would be expected. Also, more than half of the selected studies were published since 2016, corroborating that this topic is very recent and has a high potential for additional research.

心房颤动（AF）是一种以心律不齐为特征的心律失常类型，有四种类型，其中两种类型很难通过标准技术（例如心电图（ECG））进行诊断。但是，由于智能可穿戴设备已越来越成为一种日常设备，因此有多种方法可以仅使用ECG检查来检测和预测AF发作，从而使医生更容易诊断。通过搜索几个数据库，本研究对过去十年中发表的文章进行了回顾，重点关注那些使用人工智能（AI）进行AF预测研究的患者。结果表明，只有12项研究被选为系统评价，其中3项应用了深度学习技术（25％），其中有六个使用机器学习方法（占50％），其他三个则专注于应用通用人工智能模型（占25％）。总而言之，这项研究表明，在AI的背景下，对AF的预测仍然是一个尚未开发的领域，深度学习技术正在提高准确性，但这些应用并不像预期的那样频繁。此外，自2016年以来，超过一半的选定研究已经发表，这证实了该主题是最近的，并且具有进行进一步研究的巨大潜力。