The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.
The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.
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DOI：
10.3389/frai.2021.652669
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发表时间：
2021
影响因子：
4
通讯作者：
Petersen SE
中科院分区：
文献类型：
--
作者：
Abdulkareem M;Petersen SE
新冠肺炎造成了巨大的痛苦，影响了人们的生活，造成了死亡。这种冠状病毒可以很容易地传播，暴露了世界各地许多医疗系统的弱点。自从它出现以来，世界各地的许多政府、研究界、商业企业和其他机构和利益攸关方一直在以各种方式进行斗争，以遏制疾病的传播。科学和技术帮助执行了许多国家政府的政策，这些政策旨在减轻这一大流行病的影响，并对该疾病进行诊断和提供护理。最近的技术工具，特别是人工智能(AI)工具也得到了探索，以跟踪冠状病毒的传播，识别具有高死亡风险的患者，并诊断患者的疾病。本文讨论了人工智能技术在抗击新冠肺炎的检测、诊断和流行病学预测、预测和社会控制中的应用领域，强调了成功应用的领域，并强调了需要解决的问题，以在抗击新冠肺炎和未来大流行方面取得重大进展。已经开发了几个人工智能系统，用于使用胸部CT和X射线图像等医学成像模式诊断新冠肺炎。这些人工智能系统主要在图像分割、分类和疾病诊断算法的选择上有所不同。其他基于人工智能的系统一直专注于预测死亡率、长期患者住院时间和新冠肺炎的患者结果。人工智能在抗击新冠肺炎大流行的斗争中具有巨大的潜力，但这些基于人工智能的工具的成功实际部署迄今有限，原因是数据可访问性有限，需要对人工智能模型进行外部评估，人工智能专家对规范医疗保健领域人工智能工具部署的监管格局缺乏认识，临床医生和其他专家需要在多学科背景下与人工智能专家合作，以及需要解决公众对数据收集、隐私和保护的担忧。拥有一支在医疗数据收集、隐私、访问和共享方面拥有专业知识的专业团队，使用联合学习，即人工智能科学家将训练算法移交给医疗机构在本地训练模型，并充分利用存储在生物库中的生物医学数据，可以缓解这些挑战带来的一些问题。应对这些挑战最终将加速将人工智能研究转化为抗击流行病的实用和有用的解决方案。
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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DOI：
10.3390/ijerph17093176
发表时间：
2020-05-01
影响因子：
--
作者：
Bragazzi, Nicola Luigi;Dai, Haijiang;Wu, Jianhong
通讯作者：
Wu, Jianhong
影响因子：
8
作者：
Barandela, R;Sánchez, JS;Rangel, E
通讯作者：
Rangel, E
影响因子：
4.1
作者：
Almogahed, B. A.;Kakadiaris, I. A.
通讯作者：
Kakadiaris, I. A.
影响因子：
7.5
作者：
Bennett, Casey C.;Hauser, Kris
通讯作者：
Hauser, Kris
影响因子：
5.3
作者：
Alsalem, M. A.;Zaidan, A. A.;Mohammed, K. I.
通讯作者：
Mohammed, K. I.