Towards clinical AI fairness: A translational perspective
This addresses fairness issues in clinical AI applications, but it is incremental as it focuses on discussion and advocacy rather than new methods or data.
The paper tackles the problem of AI fairness in healthcare by highlighting the misalignment between technical and clinical perspectives and advocating for multidisciplinary collaboration to bridge this gap, without presenting specific numerical results.
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.