Integrative Biomedical Research (Former Journal of Angiotherapy)
Integrative Biomedical Research | Online ISSN  2207-872X
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Contents Vol 8 (8)

Influence of Artificial Intelligence and Machine Learning in Predicting and Preventing Hospital-acquired Infections: A Comprehensive Review

Jameela Ahmed Alshehri 1*, Jawaher Jamaan Aldawsari 1, Seham Moteeb Al-Motairi 1, Samia hassan Alfifi 1, Amjad Aiaid Almutairi 1, Maha Mahdi Alanizi 1, Roseline Deline Davids 1, Shaden Alzahrani 1, Abrar Abdullah Ibrahim AlFardan 1, Muted Marshd Alanazi 2, Almontashri Ali Abdullah 2, Abdulrahman Saad Marzouq Albagami 2

+ Author Affiliations

Integrative Biomedical Research (Former Journal of Angiotherapy) 8 (8) 1-9 https://doi.org/10.25163/angiotherapy.8810275

Submitted: 02 May 2024 Revised: 11 August 2024  Published: 14 August 2024 

Abstract

Hospital-acquired infections (HAIs) are clinical and financial burdens, leading to augmented morbidity, mortality, and healthcare expenses. Artificial intelligence (AI), through its machine learning (ML) and natural language processing (NLP) functionalities, has the potential to transform HAI prevention via risk prediction and early identification. This integrative review combines evidence from primary research, systematic reviews, and scoping reviews between 2010 and 2023 to establish the predictive accuracy and clinical usability of AI in HAIs control. AI models have demonstrated excellent accuracy, with AUC measures greater than 0.80 for conditions like surgical site infections (SSIs) and urinary tract infections (UTIs). Implementation is hindered by problems with EHR integration, standardization of data, and trust among clinicians. Solutions include recommended standard validation processes, interoperable technology, and clinician education to optimize the impact of AI on patient safety and infection prevention.

Keywords: Hospital-acquired infections, artificial intelligence, machine learning, review, surgical site infections.

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