A scoping review and evidence gap analysis of clinical AI fairness.

Journal: NPJ digital medicine
Published Date: Jun 14, 2025

Abstract

The ethical integration of artificial intelligence (AI) in healthcare necessitates addressing fairness. AI fairness involves mitigating biases in AI and leveraging AI to promote equity. Despite advancements, significant disconnects persist between technical solutions and clinical applications. Through evidence gap analysis, this review systematically pinpoints the gaps at the intersection of healthcare contexts-including medical fields, healthcare datasets, and bias-relevant attributes (e.g., gender/sex)-and AI fairness techniques for bias detection, evaluation, and mitigation. We highlight the scarcity of AI fairness research in medical domains, the narrow focus on bias-relevant attributes, the dominance of group fairness centering on model performance equality, and the limited integration of clinician-in-the-loop to improve AI fairness. To bridge the gaps, we propose actionable strategies for future research to accelerate the development of AI fairness in healthcare, ultimately advancing equitable healthcare delivery.

Authors

  • Mingxuan Liu
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Yilin Ning
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
  • Salinelat Teixayavong
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Xiaoxuan Liu
    Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham Reino Unido Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, Reino Unido.
  • Mayli Mertens
    Centre for Ethics, Department of Philosophy, University of Antwerp, Antwerp, Belgium; Antwerp Center on Responsible AI, University of Antwerp, Antwerp, Belgium.
  • Yuqing Shang
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Di Miao
    School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, China.
  • Jingchi Liao
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
  • Jie Xu
    Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province 310000, China.
  • Daniel Shu Wei Ting
    Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore.
  • Lionel Tim-Ee Cheng
    Department of Diagnostic Radiology, Singapore General Hospital, Singapore General Hospital, Block 2, Level 1 Outram Road, Singapore, 169608, Singapore.
  • Jasmine Chiat Ling Ong
    Duke-NUS Medical School, Singapore, Singapore.
  • Zhen Ling Teo
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Ting Fang Tan
    Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore.
  • Narrendar RaviChandran
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Leo Anthony Celi
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Marcus Eng Hock Ong
    Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore. marcus.ong.e.h@sgh.com.sg.
  • Nan Liu
    Duke-NUS Medical School Centre for Quantitative Medicine Singapore Singapore.

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