Final Paper Submission- Ivadny Ochoa Rembis -2

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Arizona State University *

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320

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Medicine

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Jun 19, 2024

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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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