Towards clinical AI fairness: A translational perspective
- URL: http://arxiv.org/abs/2304.13493v1
- Date: Wed, 26 Apr 2023 12:38:40 GMT
- Title: Towards clinical AI fairness: A translational perspective
- Authors: Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie
Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen
Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi,
Marcus Eng Hock Ong, Nan Liu
- Abstract summary: We discuss the misalignment between technical and clinical perspectives of AI fairness.
We advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions.
- Score: 13.061383127966872
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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