{"title":"Application of Artificial Intelligence in Oncology Nursing: A Scoping Review.","authors":"Tianji Zhou, Yuanhui Luo, Juan Li, Hanyi Zhang, Zhenyu Meng, Wenjin Xiong, Jingping Zhang","doi":"10.1097/NCC.0000000000001254","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing.</p><p><strong>Objective: </strong>The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing.</p><p><strong>Methods: </strong>A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022.</p><p><strong>Results: </strong>A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed.</p><p><strong>Conclusions: </strong>The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed.</p><p><strong>Implications for practice: </strong>This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.</p>","PeriodicalId":50713,"journal":{"name":"Cancer Nursing","volume":" ","pages":"436-450"},"PeriodicalIF":2.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/NCC.0000000000001254","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/5/31 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
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Abstract
Background: Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing.
Objective: The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing.
Methods: A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022.
Results: A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed.
Conclusions: The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed.
Implications for practice: This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.
期刊介绍：
Each bimonthly issue of Cancer Nursing™ addresses the whole spectrum of problems arising in the care and support of cancer patients--prevention and early detection, geriatric and pediatric cancer nursing, medical and surgical oncology, ambulatory care, nutritional support, psychosocial aspects of cancer, patient responses to all treatment modalities, and specific nursing interventions. The journal offers unparalleled coverage of cancer care delivery practices worldwide, as well as groundbreaking research findings and their practical applications.
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