Final Paper Submission- Ivadny Ochoa Rembis -2
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Arizona State University *
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Course
320
Subject
Medicine
Date
Jun 19, 2024
Type
Pages
8
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Ochoa Rembis 1
Eliminating racial bias in health care AI: Expert panel offers guidelines
Ivadny Ochoa Rembis
Arizona State University
MED 320
Dr. Rollin Medcalf
April 14, 2024
Ochoa Rembis 2
Introduction
The article "Eliminating racial bias in health care AI: Expert panel offers guidelines"
published in the
JAMA Network Open
addresses the critical issue of bias in healthcare algorithms
and offers a structured framework to mitigate these biases. It emphasizes the significant impact
that bias in algorithm development and application can have on racial and ethnic minoritized
groups, leading to disparities in healthcare outcomes. According to the article, “Health care
algorithms, defined as mathematical models used to inform decision-making, are ubiquitous and
may be used to improve health outcomes. However, algorithmic bias has harmed minoritized
communities in housing, banking, and education, and health care is no different” (Marshall
2023). The panel of experts convened by the Agency for Healthcare Research and Quality and
the National Institute for Minority Health and Health Disparities proposes a comprehensive
approach to promote equity in healthcare through the algorithm lifecycle, from development to
deployment and monitoring. The panel developed a conceptual framework that applies these
principles across the algorithm's life cycle, focusing on health and healthcare equity for patients
and communities within the broader context of structural racism and
discrimination
. The article
highlights the importance of multiple stakeholders' collaboration in mitigating and preventing
algorithmic
bias,
including
problem
formulation,
data
selection,
algorithm
development,
deployment, and monitoring. Addressing algorithmic bias is urgent, as highlighted by a Biden
Administration Executive Order aimed at preventing and remedying
discrimination
, including
protection from algorithmic
discrimination
. The article provides examples of biased algorithms
in healthcare that have resulted in disparities in treatment and access to services for racial and
ethnic minoritized groups. To finalize, the article presents a call to action for stakeholders to
implement the guiding principles and create a framework that supports health and healthcare
Ochoa Rembis 3
equity, transparency, community engagement, identification of fairness issues, and accountability
in all phases of the health care algorithm life cycle.
Discussion
The ethical issue at the core of the article "Eliminating racial bias in health care AI:
Expert panel offers guidelines" revolves around the presence and impact of racial and ethnic bias
in healthcare algorithms. These biases, when embedded in algorithms used for diagnosis,
treatment, prognosis, risk stratification, and allocation of healthcare resources, can lead to
disparate and inequitable health outcomes for racial and ethnic minority groups. Yale School of
Medicine states the following… “Artificial intelligence (AI) is revolutionizing the way clinicians
make decisions about patient care. But health care algorithms that power AI may include bias
against underrepresented communities and thus amplify existing racial inequality in medicine,
according to a growing body of evidence” (Backman 2023). The use of biased algorithms in
healthcare settings can exacerbate existing health disparities and inequalities, leading to worse
outcomes for historically marginalized populations. This goes against the principle of
justice in
healthcare
, which demands that all individuals have equal access to care and the benefits of
medical advancements, regardless of their racial or ethnic background. The ethical issue also
encompasses the lack of transparency and accountability in the development and deployment of
healthcare
algorithms
.
Without
clear
standards
for
transparency,
it's
challenging
for
stakeholders, including patients and healthcare providers, to understand how decisions are made
by these algorithms and to trust their fairness and accuracy. This lack of transparency can
undermine the ethical principle of
autonomy
, where patients have the right to be informed and
make decisions about their healthcare based on clear, accurate, and unbiased information. The
article addresses these ethical concerns by proposing a framework and guiding principles aimed
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