Ethical Artificial Intelligence In Medicine: A Case-Based Guide to Patient Safety, Equity, Trust, and Governance in Clinical AI - Couverture souple

Vale, Alan C.; Halden, Miriam E.; Ashford, Leona R.; Rowan, Caleb T.; Vey, Darius N.

Couverture souple

ISBN 13 : 9798162426738

Editeur : Independently published, 2026

Synopsis

When an algorithm influences care, how do you decide what deserves your confidence and who must answer when something goes wrong?
You need more than model accuracy to judge a system that can change referrals, triage, documentation, or access to treatment. You need a way to connect technical evidence with professional duties and the people affected by each decision.
In Ethical Artificial Intelligence in Medicine, you work through 16 chapters built around fictional clinical cases, following decisions from data collection and validation to procurement, deployment, monitoring, and retirement. You examine a primarily United States context alongside international comparisons, with six practical appendices that help you turn difficult questions into documented assessments and review processes.
Inside, you will learn to:

• Build a foundation in bioethics and professional responsibility so you can weigh autonomy, benefit, harm, and justice when reasonable goals conflict and the evidence remains incomplete.
• Examine health informatics and algorithm oversight so you can distinguish discrimination, calibration, and clinical utility, recognize dataset shift, and ask what an output means for a particular setting.
• Assess bias audits and subgroup fairness assessment so you can investigate representation, missing data, measurement choices, and unequal effects rather than relying on an overall performance score.
• Strengthen vendor procurement due diligence with questions about intended use, supporting evidence, contracts, change control, and exit planning before a product becomes embedded in routine care.
• Clarify data privacy, consent, and confidentiality so you can examine secondary use, reidentification risk, data rights, and the responsibilities that follow information across organizations.
• Design meaningful human review and incident response so you can challenge predictions, examine generative documentation errors, communicate uncertainty, and use monitoring records to support correction and organizational learning.

Whether you teach, deliver care, develop systems, or lead institutional review, you can use the cases and appendices to structure your next discussion.
Start building a more accountable approach to clinical AI with your copy.

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2026
• Langue: anglais
• ISBN 13: 9798162426738
• Reliure: Taschenbuch
• Nombre de pages: 528
• Coordonnées du fabricant: Printed by Amazon on behalf of the author; https://www.amazon.de/hz/contact-us; ; c/o Amazon Media EU S.à.r.l., 38 Avenue John F. Kennedy; Luxembourg; L-1855; Luxembourg