Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach.

Journal: BMC medical informatics and decision making
Published Date: Jan 17, 2025

Abstract

BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.

Authors

  • Tianshu Gu
    School of Mechanic Engineering, Northeast Electric Power University, Jilin, 132012, China.
  • Wensen Pan
    Department of Respiration and Intensive Care, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Jing Yu
    Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Guang Ji
    4 China-Canada Centre of Research for Digestive Diseases, University of Ottawa , Ottawa, Canada .
  • Xia Meng
    School of Public Health, Fudan University, Shanghai 200032, People's Republic of China.
  • Yongjun Wang
    Department of Neurology, Beijing Tiantan Hospital, Beijing, China.
  • Minghui Li
    MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China.

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