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Artificial intelligence and healthca...

Williams, Natasha H.

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Artificial intelligence and healthcare = the impact of algorithmic bias on health disparities /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence and healthcare/ by Natasha H. Williams.
Reminder of title:	the impact of algorithmic bias on health disparities /
Author:	Williams, Natasha H.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	x, 114 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1. Artificial Intelligence and Algorithmic Bias -- Chapter 2. What are Health Disparities -- Chapter 3. The Inclusion of Racial and Ethnic Minority Groups Participation in Clinical Trials -- Chapter 4. The Impact of Implicit Bias on Data Diversity -- Chapter 5. Will Artificial Intelligence Improve Health Disparities? -- Chapter 6. Artificial Intelligence and Health Disparities: Policy, Regulation, and Implications.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications. -
Online resource:	https://doi.org/10.1007/978-3-031-48262-5
ISBN:	9783031482625

Artificial intelligence and healthcare = the impact of algorithmic bias on health disparities /
Williams, Natasha H.

Artificial intelligence and healthcarethe impact of algorithmic bias on health disparities /[electronic resource] :by Natasha H. Williams. - Cham :Springer International Publishing :2023. - x, 114 p. :ill. (some col.), digital ;24 cm. - International library of bioethics,v. 1072662-9194 ;. - International library of bioethics ;v. 107..

Chapter 1. Artificial Intelligence and Algorithmic Bias -- Chapter 2. What are Health Disparities -- Chapter 3. The Inclusion of Racial and Ethnic Minority Groups Participation in Clinical Trials -- Chapter 4. The Impact of Implicit Bias on Data Diversity -- Chapter 5. Will Artificial Intelligence Improve Health Disparities? -- Chapter 6. Artificial Intelligence and Health Disparities: Policy, Regulation, and Implications.

This book explores the ethical problems of algorithmic bias and its potential impact on populations that experience health disparities by examining the historical underpinnings of explicit and implicit bias, the influence of the social determinants of health, and the inclusion of racial and ethnic minorities in data. Over the last twenty-five years, the diagnosis and treatment of disease have advanced at breakneck speeds. Currently, we have technologies that have revolutionized the practice of medicine, such as telemedicine, precision medicine, big data, and AI. These technologies, especially AI, promise to improve the quality of patient care, lower health care costs, improve patient treatment outcomes, and decrease patient mortality. AI may also be a tool that reduces health disparities; however, algorithmic bias may impede its success. This book explores the risks of using AI in the context of health disparities. It is of interest to health services researchers, ethicists, policy analysts, social scientists, health disparities researchers, and AI policy makers.

ISBN: 9783031482625

Standard No.: 10.1007/978-3-031-48262-5doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.28563

Artificial intelligence and healthcare = the impact of algorithmic bias on health disparities /
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520 $a This book explores the ethical problems of algorithmic bias and its potential impact on populations that experience health disparities by examining the historical underpinnings of explicit and implicit bias, the influence of the social determinants of health, and the inclusion of racial and ethnic minorities in data. Over the last twenty-five years, the diagnosis and treatment of disease have advanced at breakneck speeds. Currently, we have technologies that have revolutionized the practice of medicine, such as telemedicine, precision medicine, big data, and AI. These technologies, especially AI, promise to improve the quality of patient care, lower health care costs, improve patient treatment outcomes, and decrease patient mortality. AI may also be a tool that reduces health disparities; however, algorithmic bias may impede its success. This book explores the risks of using AI in the context of health disparities. It is of interest to health services researchers, ethicists, policy analysts, social scientists, health disparities researchers, and AI policy makers.
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