Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review
,
Abstract
Artificial intelligence applications are known to facilitate the diagnosis and the treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted between April 25 and May 6, 2020 by scanning national and international studies accessed in "Web of Science, Google Scholar, Pubmed and Scopus" databases with the keywords ("Coronavirus” or “COVID-19") and ("artificial intelligence" or “deep learning” or “machine learning”). As a result of the scanning process, 1495 (Google Scholar: 1400, Pubmed: 58, Scopus: 30, WOS: 7) studies were accessed. The studies were first examined according to their titles, and 1385 studies, which were not related to the research topic, were not included in the scope of the research. 50 articles, which did not meet the inclusion criteria, were excluded. The abstract and complete texts of the remaining 60 studies were scanned for the study's inclusion and exclusion criteria. A total of 10 studies, consisting of reviews, letters to the editor, meta-analysis studies, animal studies, conference presentations, studies not related to COVID-19, and incomplete studying protocols, were excluded. There were 50 studies left. 9 articles with duplication were identified and excluded. The remaining 41 studies were examined in detail. A total of 26 researches were found to meet the criteria for the systematic review study. In this systematic review, AI applications were found the be effective in COVID-19 diagnosis, classification, epidemiological estimates, mode of transmission, distribution and density of lesions, case increase estimation, mortality/mortality risk and early scans.
1. WHO. Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020. (Accessed April 25, 2020, at https://www.who.int/dg/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020).
2. WHO. Coronavirus disease 2019 (COVID-19) Situation Report-135. (Accessed June 3, 2020, at https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200603-covid-19-sitrep-135.pdf?sfvrsn=39972feb_2).
3. Ministry of Health of the Republic of Turkey. COVID-19 New Coronavirus Disease. (Accessed June 3, 2020, at https://covid19bilgi.saglik.gov.tr/tr/).
4. Lin B, Wun SJ. COVID-19 (Coronavirus Disease 2019): Opportunities and challenges for digital health and the ınternet of medical things in China. OMICS A Journal of Integrative Biology 2020;27(5):1-2.
5. Naudé W. Artificial intelligence against COVID-19: An early review. IZA Institute of Labor Economics Discussion Paper Series IZA DP No. 13110, 2020.
6. Broad WJ (2020). AI versus the coronavirus. The New York Times, March 26th. (Accessed April 28, 2020, at https://www.nytimes.com/2020/03/26/science/ai-versus-the-coronavirus.html ).
7. Taulli T. AI (Artificial Intelligence) companies that are combating the COVID-19 pandemic. Forbes, 28 March 2020.
8. Allam Z, Tegally H, Thondoo M. Redefining the use of big data in urban health for increased liveability in smart cities. Smart Cities 2019;2:259–268.
9. Allam Z, Dey G, Jones DS. Artificial Intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI 2020;1(2):156-165.
10. Martins, N. How healthcare ıs using big data and AI to cure disease. (Accessed April 27, 2020, at https://www.forbes.com/sites/nicolemartin1/2019/08/30/how-healthcare-is-using-big-data-and-ai-to-cure-disease/#7ebe957645cf
11. Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the landscape of Artificial Intelligence applications against COVID-19. arXiv preprint arXiv: 2020; 2003.11336.
12. Bowles J. How Canadian AI start-up Bluedot spotted coronavirus before anyone else had a clue. (Accessed April 25, 2020, at https://diginomica.com/how-canadian-ai-start-bluedot-spotted-coronavirus-anyone-else-had-clue ).
13. Metabiota. Confronting the risk you can’t see. (Accessed April 25, 2020, at http://metabiota.com/ ).
14. Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. Preprints 2020.
15. van der Schaar, M., Alaa, A., Floto, A., Gimson, A., Scholtes, S., Wood, A., et al. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. (Accessed May 6, 2020, at http://www.vanderschaar-lab.com/NewWebsite/covid-19/post1/paper.pdf ).
16. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health 2020;17(9):3176.
17. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: A population-level observational study. Lancet Digital Health 2020;2(4):e201-e208.
18. Bhattacharya S, Singh A, Hossain MM. Strengthening public health surveillance through blockchain technology. AIMS Public Health ep 2019;6(3):326-333.
19. Mashamba-Thompson TP, Crayton ED. Blockchain and artificial ıntelligence technology for novel coronavirus disease-19 self-testing. Diagnostics 2020;10:198.
20. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020;14(4):337-339.
21. Biswas K, Sen P. Space-time dependence of coronavirus (COVID-19) outbreak. arXiv preprint arXiv 2020:2003.03149.
22. Ting DS, Carin L, Dzau V, Wong TY. Digital technology and COVID-19. Nat Med Mar 2020;27:1-3.
23. Gupta R, Misra A. Contentious issues and evolving concepts in the clinical presentation and management of patients with COVID-19 infection with reference to use of therapeutic and other drugs used in Co-morbid diseases (Hypertension, diabetes etc.). Diabetes, Metab Syndrome: Clin Res Rev 2020;14(3):251- 254.
24. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta‐analyses: the PRISMA Statement. Open Medicine 2009;3(2):123-130.
25. Soares F, Villavicencio A, Fogliatto FS, Rigatto MHP, Anzanello MJ, Idiart M, et al. A novel specific artificial intelligence-based method to identify {COVID}-19 cases using simple blood exams. MedRxiv 2020.
26. Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, et al. A Novel Triage Tool of Artificial Intelligence Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever Clinics. SSRN Electronic Journal 2020.
27. Marini M, Chokani N, Abhari RS. COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data. MedRxiv 2020.
28. Pourhomayoun M, Shakibi M. Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making. MedRxiv 2020.
29. Hassanien AE, Salam A, Darwish A. Artificial intelligence approach to predict the COVID-19 patient’s recovery. EasyChair Preprint 2020;3223.
30. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, et al. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Computers, Materials & Continua 2020;62(3):537–551.
31. Mei X, Lee H, Diao K, Huang M, Lin B, Liu C, et al. Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19). MedRxiv 2020.
32. Hu Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence forecasting of COVID-19 in China. 1–20. arXiv preprint arXiv 2020:2002.07112.
33. Du S, Gao S, Zhang L. CT features and artificial intelligence quantitative analysis of recovered COVID-19 patients with negative RT-PCR and clinical symptoms. Research Square 2020.
34. Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Knapp B. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. BioRxiv 2020.
35. Zeng, S., Liu, W., Yu, X., Li, Y., Gao, J., Li, J., … Xie, C. (2020). Artificial intelligence-based CT metrics in relation with clinical outcome of COVID-19 in young and middle- aged adults. Reserch Square. doi: 10.21203/rs.3.rs-24561/v1
36. Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, et al. AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT. Radiology 2020.
37. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 2020.
38. Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, et al. Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy. MedRxiv 2020.
39. Xiaowei X, Jiang X, Ma C, Du P, Li X, Lv S, et al. Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence, 2020;1–29.
40. Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 2020;140(April):109761.
41. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O. Et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 2020;121(April):103792.
42. Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Ashqar BAM. COVID-19 Detection using Artificial Intelligence. International Journal of Academic Engineering Research 2020;4(3):18–25.
43. Gueguim Kana EB, Zebaze Kana MG, Donfack Kana AF, Azanfack Kenfack RH. A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR). MedRxiv 2020.
44. Jin C, Chen W, Cao Y, Xu Z, Zhang X, et al. Development and Evaluation of an AI System for COVID-19 Diagnosis. MedRxiv 2020.
45. Prieto FA, Baltas GN, Rios-Pena L, Rodriguez P. COVID-19 impact estimation on ICU capacity at Andalusia, Spain, using Artificial Intelligence. Researc Square 2020;1–10.
46. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. MedRxiv 2020.
47. Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease 2020;12(3):165–174.
48. Kolozsvari LR, Berczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, et al. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence. MedRxiv 2020.
49. Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, et al. Predicting COVID-19 Malignant Progression with AI Techniques. SSRN Electronic Journal 2020.
50. Pirouz B, Haghshenas SS, Haghshenas SS, Piro, P. Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of Coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability (United States), 2020;12(6).
2. WHO. Coronavirus disease 2019 (COVID-19) Situation Report-135. (Accessed June 3, 2020, at https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200603-covid-19-sitrep-135.pdf?sfvrsn=39972feb_2).
3. Ministry of Health of the Republic of Turkey. COVID-19 New Coronavirus Disease. (Accessed June 3, 2020, at https://covid19bilgi.saglik.gov.tr/tr/).
4. Lin B, Wun SJ. COVID-19 (Coronavirus Disease 2019): Opportunities and challenges for digital health and the ınternet of medical things in China. OMICS A Journal of Integrative Biology 2020;27(5):1-2.
5. Naudé W. Artificial intelligence against COVID-19: An early review. IZA Institute of Labor Economics Discussion Paper Series IZA DP No. 13110, 2020.
6. Broad WJ (2020). AI versus the coronavirus. The New York Times, March 26th. (Accessed April 28, 2020, at https://www.nytimes.com/2020/03/26/science/ai-versus-the-coronavirus.html ).
7. Taulli T. AI (Artificial Intelligence) companies that are combating the COVID-19 pandemic. Forbes, 28 March 2020.
8. Allam Z, Tegally H, Thondoo M. Redefining the use of big data in urban health for increased liveability in smart cities. Smart Cities 2019;2:259–268.
9. Allam Z, Dey G, Jones DS. Artificial Intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI 2020;1(2):156-165.
10. Martins, N. How healthcare ıs using big data and AI to cure disease. (Accessed April 27, 2020, at https://www.forbes.com/sites/nicolemartin1/2019/08/30/how-healthcare-is-using-big-data-and-ai-to-cure-disease/#7ebe957645cf
11. Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the landscape of Artificial Intelligence applications against COVID-19. arXiv preprint arXiv: 2020; 2003.11336.
12. Bowles J. How Canadian AI start-up Bluedot spotted coronavirus before anyone else had a clue. (Accessed April 25, 2020, at https://diginomica.com/how-canadian-ai-start-bluedot-spotted-coronavirus-anyone-else-had-clue ).
13. Metabiota. Confronting the risk you can’t see. (Accessed April 25, 2020, at http://metabiota.com/ ).
14. Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. Preprints 2020.
15. van der Schaar, M., Alaa, A., Floto, A., Gimson, A., Scholtes, S., Wood, A., et al. (2020). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. (Accessed May 6, 2020, at http://www.vanderschaar-lab.com/NewWebsite/covid-19/post1/paper.pdf ).
16. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health 2020;17(9):3176.
17. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: A population-level observational study. Lancet Digital Health 2020;2(4):e201-e208.
18. Bhattacharya S, Singh A, Hossain MM. Strengthening public health surveillance through blockchain technology. AIMS Public Health ep 2019;6(3):326-333.
19. Mashamba-Thompson TP, Crayton ED. Blockchain and artificial ıntelligence technology for novel coronavirus disease-19 self-testing. Diagnostics 2020;10:198.
20. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020;14(4):337-339.
21. Biswas K, Sen P. Space-time dependence of coronavirus (COVID-19) outbreak. arXiv preprint arXiv 2020:2003.03149.
22. Ting DS, Carin L, Dzau V, Wong TY. Digital technology and COVID-19. Nat Med Mar 2020;27:1-3.
23. Gupta R, Misra A. Contentious issues and evolving concepts in the clinical presentation and management of patients with COVID-19 infection with reference to use of therapeutic and other drugs used in Co-morbid diseases (Hypertension, diabetes etc.). Diabetes, Metab Syndrome: Clin Res Rev 2020;14(3):251- 254.
24. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta‐analyses: the PRISMA Statement. Open Medicine 2009;3(2):123-130.
25. Soares F, Villavicencio A, Fogliatto FS, Rigatto MHP, Anzanello MJ, Idiart M, et al. A novel specific artificial intelligence-based method to identify {COVID}-19 cases using simple blood exams. MedRxiv 2020.
26. Feng C, Huang Z, Wang L, Chen X, Zhai Y, Zhu F, et al. A Novel Triage Tool of Artificial Intelligence Assisted Diagnosis Aid System for Suspected COVID-19 Pneumonia in Fever Clinics. SSRN Electronic Journal 2020.
27. Marini M, Chokani N, Abhari RS. COVID-19 Epidemic in Switzerland: Growth Prediction and Containment Strategy Using Artificial Intelligence and Big Data. MedRxiv 2020.
28. Pourhomayoun M, Shakibi M. Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making. MedRxiv 2020.
29. Hassanien AE, Salam A, Darwish A. Artificial intelligence approach to predict the COVID-19 patient’s recovery. EasyChair Preprint 2020;3223.
30. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, et al. Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Computers, Materials & Continua 2020;62(3):537–551.
31. Mei X, Lee H, Diao K, Huang M, Lin B, Liu C, et al. Artificial intelligence for rapid identification of the coronavirus disease 2019 (COVID-19). MedRxiv 2020.
32. Hu Z, Ge Q, Li S, Jin L, Xiong M. Artificial Intelligence forecasting of COVID-19 in China. 1–20. arXiv preprint arXiv 2020:2002.07112.
33. Du S, Gao S, Zhang L. CT features and artificial intelligence quantitative analysis of recovered COVID-19 patients with negative RT-PCR and clinical symptoms. Research Square 2020.
34. Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Knapp B. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. BioRxiv 2020.
35. Zeng, S., Liu, W., Yu, X., Li, Y., Gao, J., Li, J., … Xie, C. (2020). Artificial intelligence-based CT metrics in relation with clinical outcome of COVID-19 in young and middle- aged adults. Reserch Square. doi: 10.21203/rs.3.rs-24561/v1
36. Bai HX, Wang R, Xiong Z, Hsieh B, Chang K, Halsey K, et al. AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT. Radiology 2020.
37. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 2020.
38. Castiglioni I, Ippolito D, Interlenghi M, Monti CB, Salvatore C, Schiaffino S, et al. Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from Lombardy, Italy. MedRxiv 2020.
39. Xiaowei X, Jiang X, Ma C, Du P, Li X, Lv S, et al. Deep learning system to screen coronavirus disease 2019 pneumonia. Applied Intelligence, 2020;1–29.
40. Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 2020;140(April):109761.
41. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O. Et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 2020;121(April):103792.
42. Salman FM, Abu-Naser SS, Alajrami E, Abu-Nasser BS, Ashqar BAM. COVID-19 Detection using Artificial Intelligence. International Journal of Academic Engineering Research 2020;4(3):18–25.
43. Gueguim Kana EB, Zebaze Kana MG, Donfack Kana AF, Azanfack Kenfack RH. A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR). MedRxiv 2020.
44. Jin C, Chen W, Cao Y, Xu Z, Zhang X, et al. Development and Evaluation of an AI System for COVID-19 Diagnosis. MedRxiv 2020.
45. Prieto FA, Baltas GN, Rios-Pena L, Rodriguez P. COVID-19 impact estimation on ICU capacity at Andalusia, Spain, using Artificial Intelligence. Researc Square 2020;1–10.
46. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system in four weeks. MedRxiv 2020.
47. Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, et al. Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease 2020;12(3):165–174.
48. Kolozsvari LR, Berczes T, Hajdu A, Gesztelyi R, Tiba A, Varga I, et al. Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence. MedRxiv 2020.
49. Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, et al. Predicting COVID-19 Malignant Progression with AI Techniques. SSRN Electronic Journal 2020.
50. Pirouz B, Haghshenas SS, Haghshenas SS, Piro, P. Investigating a serious challenge in the sustainable development process: Analysis of confirmed cases of COVID-19 (new type of Coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability (United States), 2020;12(6).
| Files | ||
| Issue | Vol 60, No 7 (2022) | |
| Section | Review Article(s) | |
| DOI | https://doi.org/10.18502/acta.v60i7.10208 | |
| Keywords | ||
| COVID-19 Pandemic Artificial intelligence Deep learning Machine learning | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Özsezer G, Mermer G. Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review. Acta Med Iran. 2022;60(7):387-397.
