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AI - The Coyote Trickster: Is AI Ready for Most Health Care Uses?
Journal article   Open access   Peer reviewed

AI - The Coyote Trickster: Is AI Ready for Most Health Care Uses?

Barry R Furrow
American journal of law & medicine, v 52(1), pp 120-153
Mar 2026
DOI: https://doi.org/10.1017/amj.2026.10098
PMID: 42274343
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
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https://doi.org/10.1017/amj.2026.10098View
Published, Version of Record (VoR) Open Access via Drexel Libraries Read and Publish Program 2026 Open CC BY V4.0

Abstract

Artificial Intelligence - legislation & jurisprudence Delivery of Health Care Digital Health Humans United States
This article will focus on the use of AI tools in diagnosis and patient treatment in hospitals and health care systems. AI vendors promise efficiencies in workplaces: various forms of AI are already being developed to read x-rays and other medical scans, to diagnose a wide range of patient conditions, and to offer a partnership (or risk displacement) of physicians. AI is being pushed to transform medical diagnostics, care quality, patient safety, clinical experience, and efficiencies all over hospital operations. These AI technologies however are still novel and new, and studies proving efficacy or disclaiming it are often based on small scale studies or other limitations.I will take a quick look at the tools that comprise the use of AI in health care and the claims of effectiveness of AI alone or in partnership with physicians in making clinical decisions. I will then look at the ways in which AI can fail to meet its promises, causing serious harms. Finally, I will examine the prospects of a hybrid regulatory/liability model to regulate AI risks as hospitals and providers expand their uses of AI tools.

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