人工智能在肿瘤护理中的应用:范围综述。

IF 2.4 3区 医学 Q1 NURSING Cancer Nursing Pub Date : 2024-11-01 Epub Date: 2023-05-31 DOI:10.1097/NCC.0000000000001254
Tianji Zhou, Yuanhui Luo, Juan Li, Hanyi Zhang, Zhenyu Meng, Wenjin Xiong, Jingping Zhang
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引用次数: 0

摘要

背景:近十年来,人工智能(AI)在医疗保健领域的应用日益广泛,最近在肿瘤护理领域的应用显示出改善癌症患者护理的巨大潜力。全面总结人工智能技术在肿瘤护理中的应用进展是非常及时的:本研究旨在综合评估人工智能技术在肿瘤护理中应用的现有证据:方法:根据Arksey和O'Malley提出的、后经Joanna Briggs研究所改进的方法框架进行了范围综述。检索了2010年1月至2022年11月的6个英文数据库和3个中文数据库:本综述共收录了 28 篇文章,其中英文 26 篇,中文 2 篇。半数研究采用了描述性设计(VI 级)。最广泛使用的人工智能技术是混合人工智能方法(28.6%)和机器学习(25.0%),主要用于风险识别/预测(28.6%)。几乎一半的研究(46.4%)探讨了人工智能技术的发展阶段。很少涉及伦理问题:人工智能在肿瘤护理中的适用性和前景都很广阔,尽管这些技术在实践中的有效性还缺乏证据。仍需在现实的肿瘤护理环境中开展更多随机对照试验:本范围界定综述提供了全面的研究结果,以考虑将其转化为实践,并可为肿瘤护理领域未来的人工智能教育、研究和临床实施提供指导。
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Application of Artificial Intelligence in Oncology Nursing: A Scoping Review.

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.

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来源期刊
Cancer Nursing
Cancer Nursing 医学-护理
CiteScore
4.80
自引率
3.80%
发文量
244
审稿时长
6-12 weeks
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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