A scoping review and evidence gap analysis of clinical AI fairness - Mingxuan Liu; Yilin Ning; Salinelat Teixayavong; Xiaoxuan Liu; Mayli Mertens; Yuqing Shang; Xin Li; Di Miao; Jingchi Liao; Jie Xu; Daniel Shu Wei Ting; Lionel Tim-Ee Cheng; et al. (npj Digital Medicine)
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.