How artificial intelligence may help the Covid-19 pandemic: Pitfalls and lessons for the future.

Journal: Reviews in medical virology
Published Date:

Abstract

The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid-19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI-based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid-19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI-based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid-19 cases. AI-based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic.

Authors

  • Yashpal Singh Malik
    Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.
  • Shubhankar Sircar
    Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.
  • Sudipta Bhat
    Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.
  • Mohd Ikram Ansari
    Division of Biological Standardization, ICAR-Indian Veterinary Research Institute, Bareilly, Uttar Pradesh, India.
  • Tripti Pande
    McGill International TB Centre, McGill University, Montreal, Canada.
  • Prashant Kumar
    Global Centre for Clean Air Research (GCARE), School of Engineering, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom; Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom.
  • Basavaraj Mathapati
    Polio Virus Group, Microbial Containment Complex, I.C.M.R. National Institute of Virology, Pune, Maharashtra, India.
  • Ganesh Balasubramanian
    Laboratory Division, Indian Council of Medical Research -National Institute of Epidemiology, Ministry of Health & Family Welfare, Chennai, Tamil Nadu, India.
  • Rahul Kaushik
    Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa, Japan.
  • Senthilkumar Natesan
    Indian Institute of Public Health Gandhinagar, Gandhinagar, Gujarat, India.
  • Sayeh Ezzikouri
    Viral Hepatitis Laboratory, Virology Unit, Institut Pasteur du Maroc, Casablanca, Morocco.
  • Mohamed E El Zowalaty
    Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, UAE.
  • Kuldeep Dhama
    Indian Veterinary Research Institute (IVRI), Izatnagar, Bareilly, Uttar Pradesh.