SwePub - sökning: WFRF:(Mathapati Basavaraj)

Numrering	Referens	Omslagsbild	Hitta
1.	Malik, Yashpal Singh, et al. (författare) Pitfalls and lessons for the future : Pitfalls and lessons for the future 2021 Ingår i: Reviews in Medical Virology. - : John Wiley & Sons. - 1052-9276 .- 1099-1654. ; 31:5 Forskningsöversikt (refereegranskat)abstract The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better responses against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

Malik, Yashpal Singh, et al. (författare)
Pitfalls and lessons for the future : Pitfalls and lessons for the future
2021
Ingår i: Reviews in Medical Virology. - : John Wiley & Sons. - 1052-9276 .- 1099-1654. ; 31:5
Forskningsöversikt (refereegranskat)abstract
- The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better responses against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

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