A Survey of Bias in Healthcare: Pitfalls of Using Biased Datasets and Applications
Date Issued
2023-07-09
Author(s)
Velichkovska, Bojana
Gjoreski, Hristijan
Osmani, Venet
DOI
https://doi.org/10.1007/978-3-031-35314-7_50
Abstract
Artificial intelligence (AI) is widely used in medical applications to
support outcome prediction and treatment optimisation based on collected patient
data. With the increasing use of AI in medical applications, there is a need to
identify and address potential sources of bias that may lead to unfair decisions.
There have been many reported cases of bias in healthcare professionals, medical
equipment, medical datasets, and actively used medical applications. These cases
have severely impacted the quality of patients’ healthcare, and despite awareness
campaigns, bias has persisted or in certain cases even exacerbated. In this paper,
we survey reported cases of different forms of bias in medical practice, medical
technology, medical datasets, and medical applications, and analyse the impact
these reports have in the access and quality of care provided for certain patient
groups. In the end, we discuss possible pitfalls of using biased datasets and
applications, and thus, provide the reasoning behind the need for robust and
equitable medical technologies.
support outcome prediction and treatment optimisation based on collected patient
data. With the increasing use of AI in medical applications, there is a need to
identify and address potential sources of bias that may lead to unfair decisions.
There have been many reported cases of bias in healthcare professionals, medical
equipment, medical datasets, and actively used medical applications. These cases
have severely impacted the quality of patients’ healthcare, and despite awareness
campaigns, bias has persisted or in certain cases even exacerbated. In this paper,
we survey reported cases of different forms of bias in medical practice, medical
technology, medical datasets, and medical applications, and analyse the impact
these reports have in the access and quality of care provided for certain patient
groups. In the end, we discuss possible pitfalls of using biased datasets and
applications, and thus, provide the reasoning behind the need for robust and
equitable medical technologies.