Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays.

Journal: Scientific reports
Published Date: Jan 25, 2023

Abstract

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice's quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants' confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.

Authors

  • Susanne Gaube
    UCL Global Business School for Health, University College London, London, United Kingdom.
  • Harini Suresh
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Martina Raue
    MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Eva Lermer
    LMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
  • Timo K Koch
    LMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
  • Matthias F C Hudecek
    Department of Experimental Psychology, University of Regensburg, Regensburg, Germany.
  • Alun D Ackery
    Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Emergency Medicine, St. Michael's Hospital, Toronto, Ontario, Canada. Electronic address: alun.ackery@unityhealth.to.
  • Samir C Grover
    Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
  • Joseph F Coughlin
    MIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Dieter Frey
    LMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
  • Felipe C Kitamura
  • Marzyeh Ghassemi
    Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
  • Errol Colak
    Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

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