RT Journal Article
T1 Applications of artificial intelligence in the detection and prevention of assaults against healthcare Staff: A systematic review
A1 Moreno Moreno, Antonio, J.
A1 García Iglesias, Juan Jesús
A1 Castaño Seiquer, Antonio
A1 Ribas Pérez, David
A1 Fagundo Rivera, Javier
A1 Ruger Navarrete, Azahara
A1 Allande Cussó, Regina
A1 Gómez Salgado, Juan
AB Background: The rising incidence of physical, verbal, and psychological assaults on healthcare workers has become a critical occupational and public health concern. Such incidents negatively impact staff well-being, contribute to burnout, and compromise the overall quality of patient care. Objectives: To explore how artificial intelligence (AI) techniques, including machine learning, deep learning, and natural language processing, can be applied in healthcare settings to detect, prevent, or mitigate workplace assaults against healthcare staff. Additionally, it sought to identify the most effective technical and ethical strategies for implementing AI-based interventions.Methods: A systematic review was performed following the PRISMA 2020 guidelines. Searches were conducted between July and August 2025 in electronic databases (PubMed, Scopus, Web of Science, CINAHL, IEEE Xplorer, and Google Scholar) and AI-assisted bibliographic tools (Perplexity, SciSpace, and Elicit). Studies were screened and assessed for methodological quality using the Joanna Briggs Institute critical appraisal tools. Results: Seventeen studies met the inclusion criteria. Evidence indicates that AI can play a substantial role in enhancing staff safety. Approaches include smart sensors for real-time detection of aggressive behaviors, deep learning models analyzing clinical notes to predict violent incidents, advanced natural language processing systems (BERT, GPT, RAG-ECE) for identifying threatening language, and interpretable machine learning algorithms (LightGBM-SHAP-ALE) for pinpointing key risk factors.Conclusions: Predicting and preventing violence in healthcare is a complex challenge requiring multidisciplinary solutions. AI offers promising tools for early detection, accurate prediction, and effective intervention. However, implementation must consider technical limitations, algorithmic bias, privacy concerns, and the necessity of human oversight.
PB Elsevier
SN 0925-7535
SN 1879-1042 (electrónico)
YR 2026
FD 2026
LK https://hdl.handle.net/10272/28447
UL https://hdl.handle.net/10272/28447
LA eng
NO Moreno-Moreno, A. J., García-Iglesias, J. J., Castaño-Seiquer, A., Ribas-Pérez, D., Fagundo-Rivera, J., Ruger-Navarrete, A., Allande-Cussó, R., & Gómez-Salgado, J. (2026). Applications of artificial intelligence in the detection and prevention of assaults against healthcare Staff: A systematic review. Safety Science, 201, 107243. https://doi.org/10.1016/j.ssci.2026.107243
DS Repositorio Institucional de la Universidad de Huelva
RD 14 jul 2026