Towards clinical AI fairness: A translational perspective
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Abstract (arXiv)
Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 54 references that are in the map. First posted on arXiv 26 Apr 2023.
Builds on
11 of 54 references.
References · 11
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- 2022Error Parity Fairness: Testing for Group Fairness in Regression Taskscited by 5
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Citing papers per year: 2026: 1.
1 paper.