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

By:

Williams, Natasha H

Material type: Text

TextSeries: The International Library of Bioethics (ILB, volume 107)Publication details: Springer Cham 2023Description: x, 114 pISBN:

9783031482618

Subject(s):

DDC classification:

006.3 WILL

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-48262-5)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.3 WILL (Browse shelf(Opens below))	1	Available		007846

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Previous	006.3 TEO Artificial Intelligence with Python	006.3 THI Artificial Intelligence for internet of things: design principle, modernization and techniques	006.3 TOD The use of artificial intelligence in the public sector in shanghai: ambition, capacity and reality	006.3 WILL Artificial intelligence and healthcare: the impact of algorithmic bias on health disparities	006.3 YAM AI: unexplainable, unpredictable, uncontrollable	006.3 YAO Applied artificial intelligence: a handbook for business leaders	006.3 ZAR Virtual reality and artificial intelligence: risks and opportunities for your business	Next

Table of contents:
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.
(https://link.springer.com/book/10.1007/978-3-031-48262-5)

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.

(https://link.springer.com/book/10.1007/978-3-031-48262-5)

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