Technical/Algorithm, Stakeholder, and Society (TASS) barriers to the application of artificial intelligence in medicine: A systematic review.

Journal: Journal of biomedical informatics
Published Date: Oct 25, 2023

Abstract

INTRODUCTION: The use of artificial intelligence (AI), particularly machine learning and predictive analytics, has shown great promise in health care. Despite its strong potential, there has been limited use in health care settings. In this systematic review, we aim to determine the main barriers to successful implementation of AI in healthcare and discuss potential ways to overcome these challenges.

Authors

  • Linda T Li
    Department of Pediatric Surgery; McGovern Medical School at The University of Texas Health Science Center at Houston. Electronic address: Linda.T.Li@uth.tmc.edu.
  • Lauren C Haley
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States. Electronic address: lauren.c.haley@uth.tmc.edu.
  • Alexandra K Boyd
    McGovern Medical School at the University of Texas Health Science Center at Houston, 6431 Fannin St, Houston, TX 77030, United States. Electronic address: alexandra.k.boyd@uth.tmc.edu.
  • Elmer V Bernstam
    Center for Computational Biomedicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA, Department of Public Health Science, Medical University of South Carolina, 135 Cannon Street, Suite 303, Charleston, SC 29425, USA and Department of Investigational Cancer Therapeutics, Institute for Personalized Cancer Therapy, UT-MD Anderson Cancer Center, 1400 Holcombe Blvd., FC8.3044, Houston, TX 77030, USA.

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