• Comparing Generative Artificia...

*Result*: Comparing Generative Artificial Intelligence vs Experts for Detection of Catheter-Associated Urinary Tract Infection.

Title:
Comparing Generative Artificial Intelligence vs Experts for Detection of Catheter-Associated Urinary Tract Infection.
Authors:
Alshanqeeti S; Institute for Human Virology, University of Maryland School of Medicine, Baltimore, Maryland, USA.; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA.; Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia., Coffey K; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA.; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA., Mcdermott K; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA., de Guzman A; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA., Branch-Elliman W; Section of Infectious Disease, VA Greater Los Angeles HCS-West LA, Los Angeles, California, USA.; Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, California, USA., Goodman KE; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; University of Maryland Institute for Healthcare Computing, Bethesda, Maryland, USA., Harris AD; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA.; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; University of Maryland Institute for Healthcare Computing, Bethesda, Maryland, USA., Baghdadi J; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; University of Maryland Institute for Healthcare Computing, Bethesda, Maryland, USA., Morgan DJ; Department of Infectious Disease, VA Maryland Health Care System, Baltimore, Maryland, USA.; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Source:
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America [Clin Infect Dis] 2026 Feb 09; Vol. 82 (1), pp. 148-150.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: United States NLM ID: 9203213 Publication Model: Print Cited Medium: Internet ISSN: 1537-6591 (Electronic) Linking ISSN: 10584838 NLM ISO Abbreviation: Clin Infect Dis Subsets: MEDLINE
Imprint Name(s):
Publication: Jan. 2011- : Oxford : Oxford University Press
Original Publication: Chicago, IL : The University of Chicago Press, c1992-
MeSH Terms:
Urinary Tract Infections*/diagnosis , Catheter-Related Infections*/diagnosis , Artificial Intelligence*, Humans ; Retrospective Studies ; Sensitivity and Specificity ; Male ; Female ; Aged ; Middle Aged ; Generative Artificial Intelligence
Contributed Indexing:
Keywords: CAUTI; artificial intelligence; healthcare epidemiology; hospital-acquired infections; large language models
Entry Date(s):
Date Created: 20250909 Date Completed: 20260209 Latest Revision: 20260209
Update Code:
20260209
DOI:
10.1093/cid/ciaf486
PMID:
40923191
Database:
MEDLINE

*Further Information*

*A retrospective cohort study compared generative artificial intelligence (GenAI) versus infection control expert for catheter-associated urinary tract infection (CAUTI) detection. Sensitivity was 95.2% (20/21), and specificity was 76.2% (16/21), improving to 90% with expert review of GenAI output. GenAI is a promising tool to assist in CAUTI surveillance.
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