Artificial intelligence use and performance in detecting and predicting healthcare-associated infections: A systematic review
A growing body of evidence suggests that AI models are equal to or superior to traditional methods for HAI detection and prediction, but challenges remain in evaluating performance, with many studies lacking comparators, few prospective evaluations, and limited assessment of organisational impact.
Authors
- Luigi De Angelis (ORCID: https://orcid.org/0009-0005-6136-6596)
- Caterina Rizzo (ORCID: https://orcid.org/0000-0002-5583-7508)
- Anna Odone
- Chiara Barbati (ORCID: https://orcid.org/0009-0009-5798-3064)
- Riccardo Bellazzi (ORCID: https://orcid.org/0000-0002-6974-9808)
- Lucia Sacchi (ORCID: https://orcid.org/0000-0002-1390-9825)
- Francesco Baglivo (ORCID: https://orcid.org/0000-0001-9391-7966)
- Guglielmo Arzilli (ORCID: https://orcid.org/0000-0003-1258-1650)
- Luca Viviani (ORCID: https://orcid.org/0009-0006-6420-934X)
- Riccardo Vecchio (ORCID: https://orcid.org/0000-0002-9702-8733)
Institutions
- University of Pisa (IT)
- University of Pavia (IT)
Publication Details
- Journal
- Artificial Intelligence in Medicine
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1016/j.artmed.2025.103321
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00