Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review.

Journal: Breast cancer research and treatment
PMID: 38853221

Abstract

PURPOSE: Artificial intelligence (AI) for reading breast screening mammograms could potentially replace (some) human-reading and improve screening effectiveness. This systematic review aims to identify and quantify the types of AI errors to better understand the consequences of implementing this technology.

Authors

  • Aileen Zeng
    The Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council New South Wales, Sydney, NSW, Australia.
  • Nehmat Houssami
    Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
  • Naomi Noguchi
    a The University of Sydney, Faculty of Medicine and Health , Sydney School of Public Health (A27) , Sydney , Australia.
  • Brooke Nickel
    Wiser Healthcare, Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
  • M Luke Marinovich
    The Daffodil Centre, The University of Sydney, A Joint Venture With Cancer Council New South Wales, Sydney, New South Wales, Australia.

Keywords

External Resources

Popular Topics
Recent Journals