Artificial intelligence as a powerful tool in overcoming substantial health problems of the COVID-19 pandemic

Introduction This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approac...
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Autor*in:	Jokanovic Vukoman [verfasserIn] Živković Marija [verfasserIn] Živković Slavoljub [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; srp

Erschienen:	2021

Schlagwörter:	machine learning artificial intelligence radiology chest x-ray ct

Übergeordnetes Werk:	In: Stomatološki glasnik Srbije - Serbian Medical Society - Dental Section, Belgrade, 2018, 68(2021), 3, Seite 143-152
Übergeordnetes Werk:	volume:68 ; year:2021 ; number:3 ; pages:143-152

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.2298/SGS2103143J

Katalog-ID:	DOAJ052215393

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spelling	10.2298/SGS2103143J doi (DE-627)DOAJ052215393 (DE-599)DOAJ8d9cd040282946eaa2ac17bfa1a41c35 DE-627 ger DE-627 rakwb eng srp RK1-715 Jokanovic Vukoman verfasserin aut Artificial intelligence as a powerful tool in overcoming substantial health problems of the COVID-19 pandemic 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Introduction This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators , in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. In addition, more effort is needed to develop standardized reporting protocols or guidelines on applying artificial intelligence into conventional clinical practice. This technology is suitable for fast and accurate diagnosis, prediction and monitoring of current patients and prognosis of disease development in future patients. machine learning artificial intelligence radiology chest x-ray ct Dentistry Živković Marija verfasserin aut Živković Slavoljub verfasserin aut In Stomatološki glasnik Srbije Serbian Medical Society - Dental Section, Belgrade, 2018 68(2021), 3, Seite 143-152 (DE-627)539001198 (DE-600)2381437-8 14523701 nnns volume:68 year:2021 number:3 pages:143-152 https://doi.org/10.2298/SGS2103143J kostenfrei https://doaj.org/article/8d9cd040282946eaa2ac17bfa1a41c35 kostenfrei https://scindeks-clanci.ceon.rs/data/pdf/0039-1743/2021/0039-17432103143J.pdf kostenfrei https://doaj.org/toc/0039-1743 Journal toc kostenfrei https://doaj.org/toc/1452-3701 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 68 2021 3 143-152
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Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators , in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. 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title	Artificial intelligence as a powerful tool in overcoming substantial health problems of the COVID-19 pandemic
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abstract	Introduction This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators , in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. In addition, more effort is needed to develop standardized reporting protocols or guidelines on applying artificial intelligence into conventional clinical practice. This technology is suitable for fast and accurate diagnosis, prediction and monitoring of current patients and prognosis of disease development in future patients.
abstractGer	Introduction This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators , in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. In addition, more effort is needed to develop standardized reporting protocols or guidelines on applying artificial intelligence into conventional clinical practice. This technology is suitable for fast and accurate diagnosis, prediction and monitoring of current patients and prognosis of disease development in future patients.
abstract_unstemmed	Introduction This review aims to investigate modern methods of applying artificial intelligence to diagnose SARS Cov-2 and predict the development of potential emergencies. Methods The most commonly used electronic databases, such as Scopus and Medline during 2020, were searched. A narrative approach was used to synthesize the extracted data. Results In this review paper, it has been shown that the application of artificial intelligence plays a significant role in virus diagnosis and prognosis in clinical trials. It allows resources to be used much more rationally, such as respirators , in hospitals, during the treatment of SARS Cov-2 and the prediction of possible mortality. The obtained results are from the analysis performed on 120 papers and studies that were electronically taken from papers published on Scopus and Pub Med line. Most commonly used artificial intelligence techniques are convolutional neural networks and machine learning. Conclusions Included studies showed that artificial intelligence can significantly improve the treatment of SARS Cov-2, although many of the proposed methods have not yet been clinically accepted. In addition, more effort is needed to develop standardized reporting protocols or guidelines on applying artificial intelligence into conventional clinical practice. This technology is suitable for fast and accurate diagnosis, prediction and monitoring of current patients and prognosis of disease development in future patients.
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title_short	Artificial intelligence as a powerful tool in overcoming substantial health problems of the COVID-19 pandemic
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