Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

Journal: International orthopaedics
Published Date:

Abstract

BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests.

Authors

  • Shashank Vaid
    DeGroote School of Business, McMaster University, 1280 Main Street W, Hamilton, Ontario, L8S 4 M4, Canada. vaids1@mcmaster.ca.
  • Reza Kalantar
    The Institute of Cancer Research, Royal Cancer Hospital, London, UK.
  • Mohit Bhandari
    Department of Surgery, McMaster University, Hamilton, Ontario, Canada.