Towards clinical AI fairness: A translational perspective

by   Mingxuan Liu, et al.

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.


page 1

page 2

page 3

page 4


Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation

With increasing digitalization, Artificial Intelligence (AI) is becoming...

Algorithm Fairness in AI for Medicine and Healthcare

In the current development and deployment of many artificial intelligenc...

Inherent Limitations of AI Fairness

As the real-world impact of Artificial Intelligence (AI) systems has bee...

Towards FATE in AI for Social Media and Healthcare: A Systematic Review

As artificial intelligence (AI) systems become more prevalent, ensuring ...

The Myth of Complete AI-Fairness

The idea of fairness and justice has long and deep roots in Western civi...

A Framework for Designing Fair Ubiquitous Computing Systems

Over the past few decades, ubiquitous sensors and systems have been an i...

AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics

While Artificial Intelligence (AI) technologies are being progressively ...

Please sign up or login with your details

Forgot password? Click here to reset