COVID-19 Pandemic: How Artificial Intelligence can help us / COVID-19 Pandemia: Como a Inteligência Artificial pode nos ajudar

Oswaldo Jesus Rodrigues da Motta, Gabriel Resende Machado, Andréia Patrícia Gomes, Elen Nas, Eugênio Silva, Ronaldo Ribeiro Goldschmidt, Rodrigo Siqueira- Batista


The current pandemic of COVID-19 – Coronavirus Disease-19, caused by the pathogen SARS-CoV-2, has already killed thousands of people in the year of 2020. We did a review of literature to understand the potential use of AI to respond to COVID-19. The search strategy used five descriptors: (i) “Artificial Intelligence”, (ii) “Deep Learning”, (iii) “Machine Learning”, (iv) “COVID-19” and (v) “SARS-CoV-2”, which have been combined to search for articles in the PubMed and ResearchGate databases. The bibliographic search has been complemented with texts of previous knowledge of the authors. The subjects covered in the chosen bibliography have been organized into two main study categories: (i) AI for diagnosis; and (ii) AI for controlling dissemination. The final considerations point out the main complications for the proper use of AI and the possibilities of its application to support decision making of health professionals and at managerial levels of public health.


Artificial Intelligence, COVID-19, Machine Learning.

Texto completo:



McIntosh K. Middle East respiratory syndrome coronavirus: Clinical manifestations and diagnosis. UpToDate Oct 2018; 18.

WHO. World Health Organization. Guidelines for the global surveillance of severe acute respiratory syndrome (SARS). Updated recommendations, October 2004. Available at: Accessed in March 11, 2020.

WHO. World Health Organization. Laboratory testing for Middle East respiratory syndrome coronavirus-Interim recommendations (revised), September 2014. Available at: . Accessed in March 06, 2015.

WHO. World Health Organization. Revised case definition for reporting to WHO – Middle East respiratory syndrome coronavirus - Interim case definition as of 14 July 2015. Available at: . Accessed in June 19, 2017.

Cupertino MC, Resende MB, Mayers N, Carvalho LM, Siqueira-Batista R. Emerging and re-emerging human infectious diseases: A systematic review of the role of wild animals with a focus on public health impact. Asian Pac J Trop Med 2020; 13: 99-106.

CDC. Centers for Disease Control and Prevention. Morbidity and Mortality Weekly Report Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak — United States, December 31, 2019. 2020; 69 February 4, 2020 Early Release February 5.

Kersting K. Machine learning and artificial intelligence: two fellow travelers on the quest for intelligent behavior in machines. Front. Big Data. 2018; 1(6).

Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016.

Settles B. Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences; 2009.

Zaherr A, David SJ. On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management. Healthcare 2020; 8(46). doi:10.3390/healthcare8010046.

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.

Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. Journal of Medical Systems 2020; 44:93.

Li D, Wang D, Dong J, Wang N, Huang H, Xu H, Xia C. False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases. Korean J Radiol 2020; 21(4):505-508.

Long JB, Ehrenfeld JM. The role of augmented intelligence (ai) in detecting and preventing the spread of novel coronavirus. Journal of Medical Systems 2020; 44(59). s10916-020-1536-6.

Chen J, Wu L, Zhang J, Gong D, Zhao Y, Hu S et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. medRxiv 2020. doi:

Guo Q, Li M, Wang C, Wang P, Fang Z, Tan Z, Wu S, et al. Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm. bioRxiv. 2020. DOI:

Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. International Journal of Interactive Multimedia and Artificial Intelligence 2020; 6: 51–61.

Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports 2016; 6(1): 1-13.

Litjens G et al. A survey on deep learning in medical image analysis. Medical image analysis 2017; 42: 60-88.

Cupertino MC, Cupertino GA, Gomes AP, Mayers N, Siqueira-Batista R. COVID-19 in Brazil: epidemiological update and perspectives. Asian Pacific Journal of Tropical Medicine 2020; 13:1-4.

Ganasegeran K, Abdulrahman SA. Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics. In: Human Behaviour Analysis Using Intelligent Systems. Springer, Cham; 2020, p. 141-155.



  • Não há apontamentos.