The application and use of artificial intelligence in cancer nursing: A systematic review

心理信息 医学 梅德林 人工智能 批判性评价 系统回顾 医疗保健 乳腺癌 癌症 护理部 计算机科学 替代医学 病理 内科学 政治学 法学 经济 经济增长
作者
Siobhán O’Connor,Amy Vercell,David C. Wong,Janelle Yorke,Fatmah Fallatah,Louise Cave,Lu‐Yen Anny Chen
出处
期刊:European Journal of Oncology Nursing [Elsevier]
卷期号:68: 102510-102510 被引量:6
标识
DOI:10.1016/j.ejon.2024.102510
摘要

PurposeArtificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing.MethodsCINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.ResultsArtificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models.ConclusionElectronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.
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