review · Diagnostics
The rapid spread of COVID-19 placed substantial pressure on global healthcare systems, prompting technology companies and researchers to deploy artificial intelligence to counter the pandemic. This survey examines the central contributions of machine learning technologies across five main operational areas. These applications comprise disease diagnosis using multi-modal data including sound, text, and medical images; the forecasting of infection spread based on recorded case numbers; the evaluation of links between patient characteristics and infection; assistance with vaccine development and drug interaction studies; and the deployment of general supporting software. In addition to cataloguing these operational uses, the work provides a direct comparison of available COVID-19 datasets. It synthesises recurring shortcomings across existing literature to outline persistent technical challenges, offering guidance for subsequent development of machine learning solutions during global health crises.
Understanding how machine learning can be applied during a health crisis helps public health bodies and software developers target effective tools. By mapping the primary uses of artificial intelligence, comparing relevant data resources, and pointing out existing research gaps, this overview clarifies where computational tools can effectively assist healthcare systems and where further technical improvements are required.
The abstract identifies tools developed by commercial artificial intelligence companies across diagnostic software, epidemiological forecasting, pharmacology, and patient management. While commercial actors are actively building such applications, the presence of compared datasets and open research challenges indicates that many underlying methods remain within academic review and early-to-intermediate development rather than fully consolidated commercial products.
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Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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DOI: 10.3390/diagnostics11071155
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