Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Apr 19, 2024

Abstract

OBJECTIVES: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.

Authors

  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Liqin Wang
    Brigham and Women's Hospital, Boston, MA, USA.
  • Julie Hong
    Wellesley High School, Wellesley, MA 02481, United States.
  • Jiaqi Jiang
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.

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