Encoding Bioethics: AI in Clinical Decision-Making by Binkley, Charles

Encoding Bioethics: AI in Clinical Decision-Making

Encoding Bioethics addresses important ethical concerns from the perspective of each of the stakeholders who will develop,...
Dhs. 280.18 AED
Dhs. 280.18 AED
SKU: 9780520397538
Product Type: Books
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Author: Charles Binkley
Format: Paperback
Language: English
Subtotal: Dhs. 280.18
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Encoding Bioethics: AI in Clinical Decision-Making by Binkley, Charles

Encoding Bioethics: AI in Clinical Decision-Making

Dhs. 280.18

Encoding Bioethics: AI in Clinical Decision-Making

Dhs. 280.18
Author: Charles Binkley
Format: Paperback
Language: English
Encoding Bioethics addresses important ethical concerns from the perspective of each of the stakeholders who will develop, deploy, and use artificial intelligence systems to support clinical decisions. Utilizing an applied ethical model of patient-centered care, this book considers the viewpoints of programmers, health system and health insurance leaders, clinicians, and patients when AI is used in clinical decision-making. The authors build on their respective experiences as a surgeon-bioethicist and a surgeon-AI developer to give the reader an accessible account of the relevant ethical considerations raised when AI systems are introduced into the physician-patient relationship.

Author: Charles Binkley, Tyler Loftus
Publisher: University of California Press
Published: 06/11/2024
Pages: 248
Binding Type: Paperback
Weight: 0.60lbs
Size: 8.40h x 5.50w x 0.70d
ISBN: 9780520397538

About the Author
Charles Binkley is a liver and pancreas surgeon, bioethicist, Director of Bioethics at Hackensack Meridian Health, and Associate Professor of Surgery at Hackensack Meridian School of Medicine. His work focuses on the ethical use of AI to improve clinical decision making.

Tyler Loftus is a trauma and acute care surgeon and intensive care unit doctor at the University of Florida who is passionate about surgical education, technical skills training, and research focused on machine learning augmentation of patient-centered care.

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