International journal of palliative nursing · 2026
Integrating artificial intelligence in oncology nursing for patients receiving chemotherapy: a review article.
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Salami Ashwaq, Sohaib Ahmed
Аннотация
BACKGROUND: Artificial intelligence has the potential to improve symptom management and enable early intervention for patients receiving chemotherapy.
AIMS: To review applications of artificial intelligence in oncology nursing for chemotherapy-related symptom management, focusing on benefits, challenges and future directions.
METHODS: A structured literature review was conducted using PubMed, Scopus and Web of Science, focusing on peer-reviewed studies from the past decade addressing artificial intelligence applications in chemotherapy symptom management.
FINDINGS: Artificial intelligence technologies (including machine learning, natural language processing, clinical decision support systems and digital health tools), can support real-time symptom monitoring, prediction of adverse events and personalised interventions. Patient-generated data from self-reporting and wearable devices may enable earlier identification of complications and improve symptom tracking and patient engagement. However, most applications remain at an early stage. Key challenges include algorithmic bias, limited interpretability, data privacy and security, lack of standardisation and interoperability, workflow integration and training and infrastructure requirements.
CONCLUSIONS: Artificial intelligence could support proactive and personalised symptom management in oncology nursing through continuous monitoring, predictive assessment and earlier intervention. Successful implementation requires robust validation, representative datasets, transparent algorithms, effective data governance and integration into clinical workflows.
IMPLICATIONS FOR PRACTICE: Oncology nurses should develop skills in using artificial intelligence-enabled tools while recognising their limitations. Artificial intelligence should complement, rather than replace, clinical judgement, with further research needed to establish its effectiveness and safety in practice.
AIMS: To review applications of artificial intelligence in oncology nursing for chemotherapy-related symptom management, focusing on benefits, challenges and future directions.
METHODS: A structured literature review was conducted using PubMed, Scopus and Web of Science, focusing on peer-reviewed studies from the past decade addressing artificial intelligence applications in chemotherapy symptom management.
FINDINGS: Artificial intelligence technologies (including machine learning, natural language processing, clinical decision support systems and digital health tools), can support real-time symptom monitoring, prediction of adverse events and personalised interventions. Patient-generated data from self-reporting and wearable devices may enable earlier identification of complications and improve symptom tracking and patient engagement. However, most applications remain at an early stage. Key challenges include algorithmic bias, limited interpretability, data privacy and security, lack of standardisation and interoperability, workflow integration and training and infrastructure requirements.
CONCLUSIONS: Artificial intelligence could support proactive and personalised symptom management in oncology nursing through continuous monitoring, predictive assessment and earlier intervention. Successful implementation requires robust validation, representative datasets, transparent algorithms, effective data governance and integration into clinical workflows.
IMPLICATIONS FOR PRACTICE: Oncology nurses should develop skills in using artificial intelligence-enabled tools while recognising their limitations. Artificial intelligence should complement, rather than replace, clinical judgement, with further research needed to establish its effectiveness and safety in practice.