Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Buket Baddal, Ferdiye Taner, Dilber Uzun Ozsahin

Diagnostics 2024 review cited by 46

importance 8/10 ai assisted Artificial Intelligence in Healthcare and Education infection surveillance stage: research

Healthcare-associated infections are the most common adverse events in healthcare, and conventional surveillance is costly and labor intensive.

HAIs constitute a major global public health concern affecting patient safety.

The systematic review summarizes AI/ML applications for HAI surveillance, risk stratification, prediction and timely detection of infections.

not stated in source

The abstract states AI-supported systems have been explored for HAI-related tasks, but cites potential rather than measured outcomes.

Venue Diagnostics
Published 2024
Work type review
Authors Buket Baddal, Ferdiye Taner, Dilber Uzun Ozsahin
Citations 46 (OpenAlex, at import time)
References 187
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.3390/diagnostics14050484
Open access gold
Keywords artificial intelligence, hospital-acquired infections, surveillance, systematic review, machine learning
8 / 10

major — millions of lives, an industry or a field

Abstract states HAIs are the most common adverse events in healthcare and a major global public health concern, with surveillance costly and labor intensive; AI review covers 162 articles.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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