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.
@article{arxiv.2304.13493,
title = {Towards clinical AI fairness: A translational perspective},
author = {Mingxuan Liu and Yilin Ning and Salinelat Teixayavong and Mayli Mertens and Jie Xu and Daniel Shu Wei Ting and Lionel Tim-Ee Cheng and Jasmine Chiat Ling Ong and Zhen Ling Teo and Ting Fang Tan and Ravi Chandran Narrendar and Fei Wang and Leo Anthony Celi and Marcus Eng Hock Ong and Nan Liu},
journal= {arXiv preprint arXiv:2304.13493},
year = {2023}
}