Leveraging machine learning in nursing: innovations, challenges, and ethical insights
Aim/objectiveThis review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the pote...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1514133/full |
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| author | Sophie So Wan Yip Sheng Ning Niki Yan Ki Wong Jeffrey Chan Kei Shing Ng Bernadette Oi Ting Kwok Robert L. Anders Simon Ching Lam |
| author_facet | Sophie So Wan Yip Sheng Ning Niki Yan Ki Wong Jeffrey Chan Kei Shing Ng Bernadette Oi Ting Kwok Robert L. Anders Simon Ching Lam |
| author_sort | Sophie So Wan Yip |
| collection | DOAJ |
| description | Aim/objectiveThis review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing.BackgroundWith the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing.DesignThis narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing.MethodsInclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis.ResultsFindings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.ConclusionsML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full potential in nursing to ensure that its advancements improve patient outcomes and support nursing professionals without compromising core nursing values. |
| format | Article |
| id | doaj-art-4954b800fd5846338caee12cda3908e4 |
| institution | OA Journals |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-4954b800fd5846338caee12cda3908e42025-08-20T02:25:47ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-05-01710.3389/fdgth.2025.15141331514133Leveraging machine learning in nursing: innovations, challenges, and ethical insightsSophie So Wan Yip0Sheng Ning1Niki Yan Ki Wong2Jeffrey Chan3Kei Shing Ng4Bernadette Oi Ting Kwok5Robert L. Anders6Simon Ching Lam7School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, Hong Kong SAR, ChinaSchool of Computer Science, University of Leeds, Leeds, United KingdomSchool of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Mathematics, Faculty of Natural Sciences, Imperial College, London, United KingdomDepartment of Diagnostic Radiology, School of Clinical Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, ChinaDepartment of Ocean Science, Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, ChinaSchool of Nursing, University of Texas at El Paso, El Paso, TX, United StatesSchool of Nursing, Tung Wah College, Hong Kong, Hong Kong SAR, ChinaAim/objectiveThis review aims to provide a comprehensive analysis of the integration of machine learning (ML) (1) in nursing by exploring its implications on patient care, nursing practices, and healthcare delivery. It highlights current applications, challenges, ethical considerations, and the potential future developments of ML in nursing.BackgroundWith the advent of ML in healthcare, the nursing profession stands on the cusp of a transformative era. Despite the technological advancements, discussions on the utilization of ML in nursing, which are crucial for advancing the profession, are lacking. This review seeks to fill this gap by examining the balance between technological innovation and the human-centric nature of nursing.DesignThis narrative review employs a detailed search strategy across several databases, including PubMed, Embase, MEDLINE, Scopus, and Web of Science. It focuses on articles that were published from January 2019 to December 2023. Moreover, this review aims to illustrate the current use, challenges, and future potential of ML applications in nursing.MethodsInclusion criteria targeted articles that focus on ML application in nursing, challenges, ethical considerations, and future directions. Exclusion criteria omitted opinion pieces and nonrelevant studies. Articles were categorized into themes, such as patient care, nursing education, operational efficiency, ethical considerations, and future potential, thus facilitating a structured analysis.ResultsFindings demonstrate that ML has significantly enhanced patient monitoring, predictive analytics, and preventive care. For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. In nursing education, ML has improved simulation-based training by facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, thus reducing nurse workload and enhancing patient care outcomes. However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.ConclusionsML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. Future directions include expanding clinical and community applications, integrating emerging technologies, and enhancing nursing education. Continuous research, ethical oversight, and interdisciplinary collaboration are essential for harnessing ML's full potential in nursing to ensure that its advancements improve patient outcomes and support nursing professionals without compromising core nursing values.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1514133/fullmachine learningartificial intelligencedigital healthpredictive analyticsethical considerationsinterdisciplinary collaboration |
| spellingShingle | Sophie So Wan Yip Sheng Ning Niki Yan Ki Wong Jeffrey Chan Kei Shing Ng Bernadette Oi Ting Kwok Robert L. Anders Simon Ching Lam Leveraging machine learning in nursing: innovations, challenges, and ethical insights Frontiers in Digital Health machine learning artificial intelligence digital health predictive analytics ethical considerations interdisciplinary collaboration |
| title | Leveraging machine learning in nursing: innovations, challenges, and ethical insights |
| title_full | Leveraging machine learning in nursing: innovations, challenges, and ethical insights |
| title_fullStr | Leveraging machine learning in nursing: innovations, challenges, and ethical insights |
| title_full_unstemmed | Leveraging machine learning in nursing: innovations, challenges, and ethical insights |
| title_short | Leveraging machine learning in nursing: innovations, challenges, and ethical insights |
| title_sort | leveraging machine learning in nursing innovations challenges and ethical insights |
| topic | machine learning artificial intelligence digital health predictive analytics ethical considerations interdisciplinary collaboration |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1514133/full |
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