A systematic review of artificial intelligence applications in oncology nursing surveillance and patient monitoring
Publication Date
5-2-2026
Document Type
Article
Publication Title
International Journal of Palliative Nursing
Abstract
Background: Artificial intelligence is emerging as a valuable tool in oncology nursing surveillance by supporting early detection of patient deterioration, real-time monitoring and timely nursing interventions. Despite growing interest, evidence remains fragmented and implementation issues are not yet well established. Aim: This study systematically reviewed empirical evidence on artificial intelligence applications in oncology nursing surveillance, identified key barriers and facilitators to implementation and evaluated implications for patient safety, continuity of care and nursing quality. Methods: A systematic literature review was conducted in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Peer-reviewed studies published in English from 2015 to early 2024 were screened using predefined eligibility criteria. Of 447 records identified, 105 studies met the inclusion criteria and were included in the qualitative synthesis. Findings: The included studies showed that artificial intelligence was used across several areas of oncology nursing surveillance, including risk prediction, early warning systems, tumor detection, radiomics, symptom monitoring and patient-reported outcomes. Common artificial intelligence approaches included machine learning, deep learning, natural language processing and explainable AI. Findings suggest that these tools may improve accuracy in clinical decision support, strengthen early identification of complications and enhance monitoring efficiency. However, key barriers persisted, including data heterogeneity, small and retrospective datasets, limited external validation, workflow integration difficulties, high costs and privacy concerns. Conclusion: Artificial intelligence-enabled surveillance shows promise, but broader adoption requires validation, integration and nursing-led co-design.
First Page
249
Last Page
262
APA Citation
Mabasa, C.,
Israel, M.,
Brosola, D.,
Anacito, M.,
Herrera, M.,
Macavinta, M.,
Gatdula, R.,
Orbeta, H.,
Narvaez, R.,
&
Ferrer, M.
(5-2-2026).
A systematic review of artificial intelligence applications in oncology nursing surveillance and patient monitoring.
Faculty Research and Scholarly Works.
DOI:10.12968/ijpn.2025.0121