Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.

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Bibliographic Details
Title: Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.
Authors: Doo FX; University of Maryland-Institute for Health Computing (UM-IHC), North Bethesda, Md.; Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, 22 S Greene St, Baltimore, MD 21201., Davis MA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Conn., Poff J; Radiology Partners, Nashville, Tenn., Lui YW; Department of Radiology, New York Langone Health/Grossman School of Medicine, New York, NY., Haines K; Department of Radiology, University of Connecticut Health Center, Farmington, Conn., Towbin AJ; Department of Radiology, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio.
Source: Radiology. Artificial intelligence [Radiol Artif Intell] 2026 Jul; Vol. 8 (4), pp. e250982.
Publication Type: Journal Article; Review
Journal Info: Publisher: Radiological Society of North America, Inc Country of Publication: United States NLM ID: 101746556 Publication Model: Print Cited Medium: Internet ISSN: 2638-6100 (Electronic) Linking ISSN: 26386100 NLM ISO Abbreviation: Radiol Artif Intell Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2638-6100
DOI:10.1148/ryai.250982