  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.
