{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T08:55:52Z","timestamp":1782550552478,"version":"3.54.5"},"reference-count":88,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T00:00:00Z","timestamp":1669334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening\/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60\u201370%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.<\/jats:p>","DOI":"10.3390\/diagnostics12122943","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T02:29:23Z","timestamp":1669343363000},"page":"2943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis"],"prefix":"10.3390","volume":"12","author":[{"given":"Dilber Uzun","family":"Ozsahin","sequence":"first","affiliation":[{"name":"Department of Medical Diagnostic Imaging, College of Health Sciences, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates"},{"name":"Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-1880","authenticated-orcid":false,"given":"Nuhu Abdulhaqq","family":"Isa","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey"},{"name":"Department of Biomedical Engineering, College of Health Science and Technology, Keffi 961101, Keffi Nasarawa State, Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Berna","family":"Uzun","sequence":"additional","affiliation":[{"name":"Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey"},{"name":"Department of Statistics, Carlos III Madrid University, 28903 Getafe, Madrid, Spain"},{"name":"Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,25]]},"reference":[{"key":"ref_1","unstructured":"Cascella, M., Rajnik, M., Aleem, A., Dulebohn, S.C., and Di Napoli, R. 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