IEEE Technology and Society Magazine - September 2023 - 76

Feature
Navigating the Ethical
Landscape of AI in
Healthcare: Insights
From a Content Analysis
Yin Wu and Xiang Michelle Liu
School of Technology and Innovation
Marymount University
Arlington, VA 22207 USA
 The applicaTion of artificial intelligence (AI)
in healthcare has gained tremendous popularity
over recent years, given its enormous potential for
strengthening the quality and efficiency of healthcare
services on a global scale. However, the deployment
of AI in healthcare comes with both benefits
and risks for patients, healthcare professionals, and
the whole society. Ethical concerns, such as how
to prevent AI from perpetuating preexisting health
disparities while incorporating its full potential in a
traditional medical setting, have been pointed out
by many researchers and policymakers [1], [2]. As
regulatory frameworks and guidelines concerning
ethical issues have been published by many organizations,
there is a pressing need to present an overview
of existing documents to see what has been
achieved and what has yet to be emphasized in the
field of AI health-related applications.
This study presents an analysis of several primary
government documents and academic articles discussing
ethical principles and concerns regarding the
applications of AI in healthcare. To do this, computer-assistive
qualitative content analysis is conducted
utilizing a software program named MAXQDA, which
is a widely used and well-documented qualitative
data analysis (QDA) software. The content analysis
This article has supplementary downloadable material available at https://doi.
org/10.1109/MTS.2023.3306543, provided by the authors.
Digital Object Identifier 10.1109/MTS.2023.3306543
Date of current version: 21 September 2023.
76
0278-0097/23©2023 IEEE
examines principles considering several socio-ethical
aspects of AI applications. Relevance among different
principles and proportion of proposed practices contained
under each principle is reported. This study
aims to identify how each set of principles interactively
influences one another and the priorities of principles
that need to be addressed in future advocacy agendas.
Background of the study
There is not a single, universal definition of AI.
The working definition of AI used in this study was
adopted from the U.S. National Science and Technology
Council (NSTC), which considers AI to be
technology that " enables computers and other automated
systems to perform tasks that have historically
required human cognition and what are typically
considered human decision-making abilities " [3].
Applications of AI in the field of healthcare have been
used to perform various human capabilities, such as
speech recognition, planning, and problem-solving
[4]. AI technologies such as machine-learning (ML)
techniques are widely incorporated into medical
practices to aid in the analysis of health data, decision-making,
pattern recognition, and predictions.
For example, an AI-enabled healthcare analytics
suite is capable of predictive modeling, identifying
beneficial resources, and helping patients manage
costs [5]. Similar data-driven approaches have also
been incorporated into a variety of applications such
as wearable technologies, electronic healthcare
IEEE Technology and Society Magazine

https://orcid.org/0009-0004-6552-7863 https://orcid.org/0000-0002-1801-3171 https://doi.org/10.1109/MTS.2023.3306543 https://doi.org/10.1109/MTS.2023.3306543

IEEE Technology and Society Magazine - September 2023

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