A scoping review and evidence gap analysis of clinical AI fairness
Liu, Mingxuan Ning, Yilin Teixayavong, Salinelat Liu, Xiaoxuan Mertens, Mayli Shang, Yuqing Li, Xin Miao, Di Liao, Jingchi Xu, Jie
Liu, Mingxuan
Ning, Yilin
Teixayavong, Salinelat
Liu, Xiaoxuan
Mertens, Mayli
Shang, Yuqing
Li, Xin
Miao, Di
Liao, Jingchi
Xu, Jie
Affiliation
Duke-NUS Medical School; University of Birmingham; University Hospitals Birmingham NHS Foundation Trust; University of Antwerp; Copenhagen University; University of Florida; Singapore National Eye Centre; Singapore General Hospital; Weill Cornell Medicine; Massachusetts Institute of Technology; Beth Israel Deaconess Medical Center; Harvard T.H. Chan School of Public Health; National University of Singapore
Other Contributors
Publication date
2025-06-14
Subject
Collections
Research Projects
Organizational Units
Journal Issue
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.
Citation
Liu M, Ning Y, Teixayavong S, Liu X, Mertens M, Shang Y, Li X, Miao D, Liao J, Xu J, Ting DSW, Cheng LT, Ong JCL, Teo ZL, Tan TF, RaviChandran N, Wang F, Celi LA, Ong MEH, Liu N. A scoping review and evidence gap analysis of clinical AI fairness. NPJ Digit Med. 2025 Jun 14;8(1):360. doi: 10.1038/s41746-025-01667-2.
Type
Article