@inproceedings{d3971c187a4942b8bca9096eceedb287,
title = "Fairness Metrics in AI Healthcare Applications: A Review",
abstract = "As artificial intelligence (AI) systems increasingly become popular in the healthcare sector, it is important to ensure the output of these technologies is fair and bias-free. This paper provides a concise survey of fairness metrics applied in healthcare AI, including their mathematical representations, suitable use cases, and limitations, which are lacking in the existing literature. The study also highlights the significance of implementing fairness metrics to ensure equitable outcomes across diverse patient populations and discusses the challenges and future directions in this rapidly evolving field.",
keywords = "AI, bias, fairness metrics, healthcare, machine learning",
author = "Mienye, {Ibomoiye Domor} and Swart, {Theo G.} and George Obaido",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 25th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2024 ; Conference date: 07-08-2024 Through 09-08-2024",
year = "2024",
doi = "10.1109/IRI62200.2024.00065",
language = "English",
series = "Proceedings - 2024 IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "284--289",
booktitle = "Proceedings - 2024 IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2024",
address = "United States",
}