Artificial Intelligence to fight COVID-19 outbreak impact: an overview
Artificial Intelligence (AI) is showing its strength worldwide in the healthcare sector. Today, in the aftermath of the COVID-19 pandemic, the help of technology appears to be relevant to keep the increase in new infections stable and help medical staff in treatment. Therefore, this paper aims to investigate how AI can be employed against COVID-19 outbreak. Using a multiple case study approach, researchers find out the following insights. First, AI could be used for drugs discovery and knowledge sharing, tracking and prediction, clinical decision making and diagnosis, social distancing and medical chatbots. Second, this paper provides an in-depth analysis of international best practice for tracking contacts and social distance applications. Third, AI technologies could have a transversal impact, also focusing on prevention strategies as a new corporate social responsibility vein. In the end, this paper has theoretical and managerial implications, too. On the theoretical side, we contribute to the extensive discussion about AI and healthcare considering COVID-19 outbreak. On the practical side, we provide medical personnel and policymakers with a tool to understand artificial intelligence and focus investment choices in the practical applications analysed.
Ahuja, A.S., Reddy, V., Marques, O. (2020). Artificial intelligence and COVID-19: A multidisciplinary approach. Integrative Medicine Research, Vol. 9, Issue 3.
Bitran, H. (2019). Microsoft Healthcare Bot brings conversational AI to healthcare. Microsoft Healthcare Israel.
Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., Wu, J.(2020). How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2020, 17, 3176; doi:10.3390/ijerph17093176.
Connelly, T.M., Malik, Z., Sehgal, R. et al. (2019). The 100 most influential manuscripts in robotic surgery: a bibliometric analysis. J Robotic Surg 14, 155–165.
Crowe, S., Cresswell, K., Robertson, A. et al. (2011). The case study approach. BMC Med Res Methodol 11, 100.
Dal Mas, F., Massaro, M., Lombardi, R., & Garlatti, A. (2019). From output to outcome measures in the public sector. A structured literature review. International Journal of Organizational Analysis, 27(5), 1631–1656.
Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M., Kalpana Devi, S. (2018). A Self-Diagnosis Medical Chatbot Using Artificial Intelligence. Journal of Web Development and Web Designing, Vol.3, Issue 1.
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2007). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338–342.
Frankish, K., Ramsey, W.M., (Eds.). (2014). Introduction. In The Cambridge Handbook of Artificial Intelligence; Cambridge University Press: Cambridge, UK, 1–14.
Granello, D. H., & Wheaton, J. E. (2004). Online data collection: Strategies for research. Journal of Counseling & Development, 82(4), 387-393.
Hamid, S. (2016). The Opportunities and Risks of Artificial Intelligence in Medicine and Healthcare. CUSPE Communications.
Jamshidi, M.B. et al. (2020). Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment, in IEEE Access, vol. 8, 109581-109595, doi: 10.1109/ACCESS.2020.3001973.
Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology.
Kreuzhuber, K. (2020). How AI, Big Data and Machine Learning can be used against the Corona virus. ARS Electronica Blog, 19 March.
Kumar, A., Gupta, P.K., Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, 569-573.
Lalmuanawma, S., Hussain, J., Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals 139 (2020) 110059.
Ledford, H. (2020). Dozens of coronavirus drugs are in development — what happens next?. Nature, 581, 247-248.
Mahomed, S. (2020). COVID-19: The role of artificial intelligence in empowering the healthcare sector and enhancing social distancing measures during a pandemic. South African Medical Journal.
Massaro, M., Dumay, J., Garlatti, A., & Dal Mas, F. (2018). Practitioners' views on intellectual capital and sustainability: From a performance-based to a worth-based perspective. Journal of Intellectual Capital, 19 (2), 367–386.
McCutcheon, D. M., & Meredith, J. R. (1993). Conducting case study research in operations management. Journal of operations management, 11(3), 239-256.
Mohanty, S., Rashid, M.H., Mridul, M., Mohanty, C. (2020). Application of Artificial Intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 (2020), 1027-1031.
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228.
Naudé, W. (2020). Artificial Intelligence against COVID-19: An Early Review. IZA Discussion, Paper no. 13110, Bonn.
Naudé, W. (2020). Artificial intelligence vs COVID‑19: limitations, constraints and pitfalls. AI & Soc.
Nguyen, T. T. (2020). Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. Preprint, DOI: 10.13140/RG.2.2.36491.23846.
Nguyen T., Saputra, Y.M., Van Huynh, N., Nguyen, N.T., Khoa, T., Tuan, B., Nguyen, D., Hoang, D., Vu, T., Dutkiewicz, E., Chatzinotas, S., Ottersten, B. (2020). Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey. Cornell University Press.
Petropoulos, G. (2020). Artificial intelligence in the fight against COVID-19. Bruegel.
Pham, Q., Nguyen, D.C., Huynh-The, T., Hwang, W., Pathirana, P.N. (2020). Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts. Preprints 2020.
Rivas, A. (2020). Drones and artificial intelligence to enforce social isolation during COVID-19 outbreak. Medium Towards Data Sci.
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., & Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE reviews in biomedical engineering.
Secinaro, S., Calandra, D., Biancone, P. (2020). Reflection on Coronavirus Accounting Impact on Small and Medium Sized Enterprises (SMEs) in Europe. Vol. 15, Issue 7, 48-56.
Sun, C., & Zhai, J. (2020). The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustainable Cities and Society 62 (2020) 102390.
Tran, B.X., Vu, G.T., Ha, G.H., Vuong, Q.H., Ho, M.T., Vuong, T.T., La, V.P., Ho, M.T., Nghiem, K.C.P., Nguyen, H.L.T., Latkin, C.A., Tam, W.W.S., Cheung, N.M., Nguyen, H.K.T., Ho, C.S.H., Ho, R.C.M. (2019). Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study. Journal of Clinical Medicine. 8(3):360.
Vaishya, R., Javaid, M., Khan, I.H., Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, 337-339.
Xue, H., Li, J., Wang, Y. (2018). Review of drug repositioning approaches and resources. International Journal of Biological Sciences, Vol. 14, Issue 10, 13 July 2018, 1232-1244.
Yassine, H.M., & Shan, Z. (2020). How could artificial intelligence aid in the fight against coronavirus?. Expert Review of Anti-infective Therapy, 18:6, 493-497, DOI: 10.1080/14787210.2020.1744275.
Yin, R.K. (2014). Case study research: design and methods. Thousand Oaks, CA: Sage Publications.
Young, O.R. (2013). Compliance & public authority: A theory with international applications. Routledge.
Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., et al. (2020). Potential COVID-19 3C-like protease inhibitors designed using generative deep learning approaches. Insilico Medicine Hong Kong Ltd.