Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .

自 2020 年初以来，全世界一直面临名为冠状病毒病 (COVID-19) 的致命和高度传染性疾病，世界卫生组织于 2020 年 3 月 11 日宣布大流行。截至目前，已报告超过 2300 万例 COVID-19 阳性病例2020 年 8 月下旬。在基于人工智能的技术的支持下，胸部 X 光和计算机断层扫描等医学图像正在成为对抗 COVID-19 的主要领先临床诊断工具之一，从而可以快速做出挽救生命的决策。本文对基于 AI 的方法进行了广泛的回顾，以帮助医生全面了解基于 AI 的有效方法，以进行高效的 COVID-19 诊断。讨论了迄今为止报告的几乎所有方法及其优缺点和改进建议，包括图像采集、分割、分类和 2019 年至 2020 年间开发的后续诊断阶段。人工智能和机器学习技术推动了Covid-19 诊断的准确性，并且大多数广泛使用的深度学习方法已经实施并在少量数据下运行良好，用于 COVID-19 诊断。这篇综述对评估在从医学图像中检测 COVID-19 的过程中使用的基于 AI 的方法进行了详细的神话分析。然而，由于 Covid-19 的快速爆发，社区可用的地面实况数据集并不多。有必要结合临床专家的观察和图像信息，以进行可靠、高效的 COVID-19 诊断。本文建议未来的研究可能集中在基于多模态的模型以及如何选择最佳模型架构，AI 可以通过从多模态数据中学习来为医疗系统引入更多智能来捕捉疾病的特征以获得可靠的结果用于 COVID-19 诊断以便及时治疗。