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The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review

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dc.contributor.author Alloghani M.
dc.contributor.author Al-Jumeily D.
dc.contributor.author Aljaaf A.
dc.contributor.author Khalaf M.
dc.contributor.author Mustafina J.
dc.contributor.author Tan S.
dc.date.accessioned 2021-02-25T06:54:14Z
dc.date.available 2021-02-25T06:54:14Z
dc.date.issued 2020
dc.identifier.issn 1865-0929
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161408
dc.description.abstract © 2020, Springer Nature Switzerland AG. The proliferation of artificial intelligence and its continued development can be attributed to the pursuit of advanced machine learning techniques for handling big health data. Even though AI appears to be an independent system while considering algorithms and learning techniques, it, however, requires integration of different machine learning algorithms to enable it to handle different data structures. Notably, the number of articles addressing AI implementation from a medical research perspective are on the rise. Further, AI in medical research have machine learning component and as such relies on algorithms such as support vector machine, neural network, deep learning, and convolution neural networks. Of these algorithms, support vector machine is the most commonly used, and it has been applied in medical imaging, diagnosis and treatment of stroke as well as early detection of cancer and neurology conditions. As per the survey, AI results in higher accuracy of diagnosis and risk prediction compared to human approaches. Despite such success and promising future, AI faces regulatory and data related challenges.
dc.relation.ispartofseries Communications in Computer and Information Science
dc.subject AI medical research
dc.subject Artificial Intelligence
dc.subject Deep learning
dc.subject Machine learning
dc.title The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review
dc.type Conference Paper
dc.relation.ispartofseries-volume 1174 CCIS
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 248
dc.source.id SCOPUS18650929-2020-1174-SID85078527357


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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