Incorporating artificial intelligence in medical diagnosis: A case for an invisible and (un)disruptive approach.

Journal: Journal of evaluation in clinical practice
Published Date:

Abstract

As big data becomes more publicly accessible, artificial intelligence (AI) is increasingly available and applicable to problems around clinical decision-making. Yet the adoption of AI technology in healthcare lags well behind other industries. The gap between what technology could do, and what technology is actually being used for is rapidly widening. While many solutions are proposed to address this gap, clinician resistance to the adoption of AI remains high. To aid with change, we propose facilitating clinician decisions through technology by seamlessly weaving what we call 'invisible AI' into existing clinician workflows, rather than sequencing new steps into clinical processes. We explore evidence from the change management and human factors literature to conceptualize a new approach to AI implementation in health organizations. We discuss challenges and provide recommendations for organizations to employ this strategy.

Authors

  • Matt Sibbald
    Department of Medicine, McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
  • Laura Zwaan
    Erasmus Medical Center, Institute of Medical Education Research Rotterdam (iMERR), Rotterdam, The Netherlands.
  • Yusuf Yilmaz
    Department of Gastroenterology, School of Medicine, Recep Tayyip Erdoğan University, Rize, Turkey.
  • Sarrah Lal
    Department of Medicine, Division of Innovation and Education, McMaster University, Hamilton, ON, Canada.