Data-driven overdiagnosis definitions: A scoping review.

Journal: Journal of biomedical informatics
Published Date: Sep 27, 2023

Abstract

INTRODUCTION: Adequate methods to promptly translate digital health innovations for improved patient care are essential. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have been sources of digital innovation and hold the promise to revolutionize the way we treat, manage and diagnose patients. Understanding the benefits but also the potential adverse effects of digital health innovations, particularly when these are made available or applied on healthier segments of the population is essential. One of such adverse effects is overdiagnosis.

Authors

  • Prabodi Senevirathna
    School of Computing and Information Systems, The University of Melbourne, Melbourne, 3053, Victoria, Australia.
  • Douglas E V Pires
    Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Australia.
  • Daniel Capurro
    School of Computing and Information Systems, The University of Melbourne, Victoria, Australia; Centre for Digital Transformation of Health, Melbourne Medical School, The University of Melbourne, Victoria, Australia. Electronic address: dcapurro@unimelb.edu.au.

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