Skip to main content
SpringerLink
Log in

Recent Advancement of Artificial Intelligence in COVID-19: Prediction, Diagnosis, Monitoring, and Drug Development

  • Conference paper
  • First Online:
Innovations in VLSI, Signal Processing and Computational Technologies (WREC 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1095))

  • 83 Accesses

Abstract

This paper intends to summarize the important applications of Artificial Intelligence and Machine Learning during the COVID-19 outbreak. In recent past, world has seen a massive pandemic due to COVID-19 virus outbreak, which affected millions of people across all nations. There have been tremendous implications and applications of Artificial Intelligence and Machine Learning, along with deep learning and Neural Networks, in the field of medical science, which has expanded the traditional dynamics of medical science and enhanced the decision-making as well as treatment delivery to the patients. Artificial Intelligence- and Machine Learning-based models have provided tools in numerous fronts, through which this pandemic can be overcome. In this paper, we have emphasized on the various important applications of Artificial Intelligence- and Machine Learning-based algorithms and models in the medical field for predicting the number of COVID-19-affected patients, diagnosing and detecting the virus, monitoring its spread as well as development of drugs and vaccines.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC (2020) Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. Int J Environ Res Public Health 17(15):5330

    Google Scholar 

  • Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med 121

    Google Scholar 

  • Barlow DH (2004) Anxiety and its disorders: the nature and treatment of anxiety and panic. Guilford press

    Google Scholar 

  • Beck BR, Shin B, Choi Y, Park S, Kang K (2020)Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 18:784–790

    Google Scholar 

  • Columbus L (2018) 10 charts that will change your perspective on Artificial Intelligence's growth. Forbes. https://www.forbes.com/sites/louiscolumbus/2018/01/12/10-chartsthat-will-change-your-perspective-on-artificial-intelligences-growth/?sh=389af1974758. Last accessed 4 Sept 2022

  • Coronavirus diseases (COVID-19) in Quebec. https://www.quebec.ca/en/health/healthissues/a-z/2019-coronavirus/. Last accessed 02 Oct 2022

  • Deoras S, How ML is assisting in development of covid-19 vaccines. https://analyticsindiamag.com/how-ml-is-assisting-in-development-of-covid-19-vaccines/. Last accessed 28 Sept 2021

  • DiÅŸken G, Tüfekçi Z, Saribulut L, Çevik U (2017) A review on feature extraction for speaker recognition under degraded conditions. IETE Tech Rev 34(3):321–332

    Google Scholar 

  • Hu Z, Ge Q, Li S, Jin L, Xiong M (2020) Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:2002.07112

  • Huang L, Han R, Ai T, Yu P, Kang H, Tao Q, Xia L (2020):Serial quantitative chest CT assessment of COVID-19: a deep learning approach. Radiol Cardiothor Imag 2(2):e200075

    Google Scholar 

  • JHU: John Hopkins University (2020) COVID-19 dashboard by the center for systems science and Engineering (CSSE) at Johns Hopkins University (JHU). https://www.coronavirus.jhu.edu/map.html. Last accessed 27 Apr 2021

  • Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4)

    Google Scholar 

  • Jin S et al (2020) AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv pp. 2020.03.19.20039354

    Google Scholar 

  • Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with alphafold. Nature 596:583–589

    Google Scholar 

  • Keni R, Alexander A, Nayak PG, Mudgal J, Nandakumar K (2020) COVID-19: emergence, spread, possible treatments, and global burden. Front Public Health 8:216

    Google Scholar 

  • Khalilpourazari S, Hashemi Doulabi H (2021) Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. Ann Oper Res, pp 1–45

    Google Scholar 

  • Kose U, Grima S, Özen E, Boz H, Spiteri J, Thalassinos E (2021) Using artificial intelligence techniques for economic time series prediction. Contemp Issues Behav Fin 101:13e28

    Google Scholar 

  • Kumar A, Tripathi AR, Satapathy SC, Zhang YD (2022) SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recogn 122:108255

    Google Scholar 

  • Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry 12:651

    Google Scholar 

  • Mahajan A, Vaidya T, Gupta A, Rane S, Gupta S (2019) Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Res Stat Treat 2(2):182

    Google Scholar 

  • Mei Y, Ye DP, Jiang SZ, Liu JR (2021) A particular character speech synthesis system based on deep learning. IETE Tech Rev 38(1):184–194

    Google Scholar 

  • Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA (2021) Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN Comput Sci 2:1–13

    Google Scholar 

  • Our World in Data: COVID-19 Dataset. https://github.com/owid/covid-19data/tree/master/public/data/. Last accessed 03 Oct 2021

  • Öztürk H, Özgür A, Ozkirimli E (2018) DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 34(17):i821–i829

    Google Scholar 

  • Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med

    Google Scholar 

  • Pahuja G, Nagabhushan TN (2021) A comparative study of existing machine learning approaches for Parkinson's disease detection. IETE J Res 67(1):4–14

    Google Scholar 

  • Petropoulos G (2020) Innovation and competition policy. http://www.bruegel.org/2020/03/artificial-intelligence-in-the-fight-against-covid-19/. Last accessed 11 Sept 2021

  • Pinter G, Felde I, Mosavi A, Ghamisi P, Gloaguen R (2020) COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach. Mathematics 8(6):890

    Google Scholar 

  • Prakash KB, Imambi SS, Ismail M, Kumar TP, Pawan YN (2020) Analysis, prediction and evaluation of covid-19 datasets using machine learning algorithms. Int J 8(5)

    Google Scholar 

  • Rustam F, Reshi AA, Mehmood A, Ullah S, On BW, Aslam W, Choi GS (2020) COVID-19 future forecasting using supervised machine learning models. IEEE Access 8:101489–101499

    Google Scholar 

  • Shim J, Hong ZY, Sohn I, Hwang C (2021) Prediction of drug–target binding affinity using similarity-based convolutional neural network. Sci Rep 11(1):4416

    Google Scholar 

  • Situation Reports-WHO. https://www.who.int/emergencies/diseases/novel-coronavirus2019/situation-reports. Last accessed 03 Oct 2022

  • Sudalai Rajkumar, datasets. Available: https://www.kaggle.com/sudalairajkumar/covid19in-india. Last accessed 28 Apr 2021

  • Tuli S, Mahmud R, Tuli S, Buyya Fogbu R (2019) A blockchain-based lightweight framework for edge and fog computing. J Syst Softw

    Google Scholar 

  • Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R (2020a) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Future Gener Comput Syst 104:187–200

    Google Scholar 

  • Tuli S, Tuli S, Tuli R, Gill SS (2020b) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things 11:100222

    Google Scholar 

  • Ur A, Rahman S, Naseer I, Majeed R, Musleh D, Gollapalli MAS, Chabani S, Ibrahim N, Siddiqui SY, Khan MA (2021) Supervised machine learning-based prediction of covid-19. Comput Mater Continua 69:21–34

    Google Scholar 

  • Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial Intelligence (AI) applications for COVID-19 pandemic. Diab Metab Syndr 14(4):337–339

    Google Scholar 

  • Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1–12

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur, India

    Priya Rachel Bachan & Prakriti Kapoor

  2. Electronics and Communication Engineering, Gyan Ganga Institute of Technology and Sciences, Jabalpur, India

    Udit Narayan Bera

Authors
  1. Priya Rachel Bachan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Udit Narayan Bera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prakriti Kapoor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priya Rachel Bachan .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of North Texas, Denton, TX, USA

    Gayatri Mehta

  2. La Trobe Univeristy, Melbourne, Australia

    Nilmini Wickramasinghe

  3. NIT Jalandhar, Punjab, India

    Deepti Kakkar

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bachan, P.R., Bera, U.N., Kapoor, P. (2024). Recent Advancement of Artificial Intelligence in COVID-19: Prediction, Diagnosis, Monitoring, and Drug Development. In: Mehta, G., Wickramasinghe, N., Kakkar, D. (eds) Innovations in VLSI, Signal Processing and Computational Technologies. WREC 2023. Lecture Notes in Electrical Engineering, vol 1095. Springer, Singapore. https://doi.org/10.1007/978-981-99-7077-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7077-3_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7076-6

  • Online ISBN: 978-981-99-7077-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation