AI recognition of patient race in medical imaging: a modelling study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images.

Authors

  • Judy Wawira Gichoya
    Department of Interventional Radiology, Oregon Health & Science University, Portland, Oregon; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Ananth Reddy Bhimireddy
    Department of Radiology, Emory University, Atlanta, GA, USA.
  • John L Burns
    School of Informatics and Computing, Indiana University-Purdue University, Indianapolis, IN, USA.
  • Leo Anthony Celi
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Li-Ching Chen
    Department of Otolaryngology, Cheng Hsin General Hospital, Taipei, Taiwan.
  • Ramon Correa
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
  • Natalie Dullerud
    Department of Computer Science, University of Toronto, Toronto, ON, Canada.
  • Marzyeh Ghassemi
    Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Shih-Cheng Huang
    Stanford University, Stanford, CA, USA.
  • Po-Chih Kuo
    Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
  • Matthew P Lungren
  • Lyle J Palmer
    School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
  • Brandon J Price
    Florida State University College of Medicine, Tallahassee, FL, USA.
  • Saptarshi Purkayastha
    Indiana University School of Informatics and Computing, Indianapolis, IN, United States.
  • Ayis T Pyrros
    Dupage Medical Group, Hinsdale, IL, USA.
  • Lauren Oakden-Rayner
    School of Public Health, University of Adelaide, Adelaide, SA, Australia; Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. Electronic address: lauren.oakden-rayner@adelaide.edu.au.
  • Chima Okechukwu
    Department of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA.
  • Laleh Seyyed-Kalantari
    Computer Science, University of Toronto, Toronto, Ontario, Canada2Vector Institute, Toronto, Ontario, Canada* Corresponding author, laleh@cs.toronto.edu.
  • Hari Trivedi
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Ryan Wang
    Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.
  • Zachary Zaiman
    Department of Computer Science, Emory University, Atlanta, GA, USA.
  • Haoran Zhang
    Massachusetts Institute of Technology, Cambridge, MA, USA.