Healthcare-associated infections (HAIs) represent a significant public health concern, correlating with increased morbidity, mortality rates and healthcare expenditures. While artificial intelligence (AI) systems offer transformative potential in enhancing HAIs detection and control practices, the actual performance effectiveness of these systems remains uncertain. This systematic review updates a previously published study from 2020 and evaluates the performance of AI-based tools for surveillance, detection, and control of HAIs.PRISMA 2020 guidelines were applied. The study protocol has been registered in PROSPERO (ID: CRD42024513145). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies assessing the performance of AI-based tools to detect and control HAIs, published in English.From 8,701 articles initially identified, 4,212 records were removed due to duplication. Out of 4,489 papers screened, 147 were included. Studies reported performance measures including sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, accuracy, precision, F1 score. Significant heterogeneity was found in the types of technology, infections targeted, health care settings and data sources between studies.The increase in published evidence since the previous review reflects the growing interest and use of new technologies such as Large Language Models, showing promising performance in surveillance, early diagnosis and prediction of HAIs. However, the observed heterogeneity in study designs, targeted infections, healthcare settings, and data sources underscores the need for standardised methodologies and robust validation processes to ensure the reliability and comparability of results across different studies.• The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems.• Standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.
Impact of artificial intelligence on healthcare-associated infection control: a systematic review
Arzilli Guglielmo;De Angelis Luigi;Baglivo Francesco;Rizzo Caterina;
2024-01-01
Abstract
Healthcare-associated infections (HAIs) represent a significant public health concern, correlating with increased morbidity, mortality rates and healthcare expenditures. While artificial intelligence (AI) systems offer transformative potential in enhancing HAIs detection and control practices, the actual performance effectiveness of these systems remains uncertain. This systematic review updates a previously published study from 2020 and evaluates the performance of AI-based tools for surveillance, detection, and control of HAIs.PRISMA 2020 guidelines were applied. The study protocol has been registered in PROSPERO (ID: CRD42024513145). PubMed, Embase, Scopus and Web of Science were searched for experimental and observational studies assessing the performance of AI-based tools to detect and control HAIs, published in English.From 8,701 articles initially identified, 4,212 records were removed due to duplication. Out of 4,489 papers screened, 147 were included. Studies reported performance measures including sensitivity, specificity, positive and negative predictive values, area under the receiver operating characteristic curve, accuracy, precision, F1 score. Significant heterogeneity was found in the types of technology, infections targeted, health care settings and data sources between studies.The increase in published evidence since the previous review reflects the growing interest and use of new technologies such as Large Language Models, showing promising performance in surveillance, early diagnosis and prediction of HAIs. However, the observed heterogeneity in study designs, targeted infections, healthcare settings, and data sources underscores the need for standardised methodologies and robust validation processes to ensure the reliability and comparability of results across different studies.• The use of AI-based tools has the potential to enhance surveillance, detection, and control of healthcare-associated infections, offering a transformative impact on healthcare systems.• Standardised methodologies and validation processes are needed to ensure comparable results across studies and to maximise the real-world impact of AI tools in HAI surveillance and control efforts.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
