REVIEW article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1514133
This article is part of the Research TopicEnhancing Patient Care: Artificial Intelligence in NursingView all articles
Leveraging Machine Learning in Nursing: Innovations, Challenges, and Ethical Insights
Provisionally accepted
Sophie So Wan So1Niki Yan Ki Yan2
Bernadette Oi Ting Oi3Jeffrey Chan4
Kei Shing Ng5Robert L Anders6
Simon Ching LAM7*- 1School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, Hong Kong, SAR China
- 2School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
- 3Hong Kong University of Science and Technology, Kowloon, Hong Kong, SAR China
- 4King George V School, Hong Kong, Hong Kong, SAR China
- 5Department of Diagnostic Radiology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
- 6School of Public Health, The University of Texas at El Paso, El Paso, Texas, United States
- 7School of Nursing, Tung Wah College, Kowloon, Hong Kong, SAR China
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The objective of this review is to provide a comprehensive analysis of the integration of machine learning (1) in nursing, exploring its implications on patient care, nursing practices, and healthcare delivery. It aims to highlight current applications, challenges, ethical considerations, and the potential future developments of ML in nursing. compliance. In nursing education, ML has improved simulation-based training, facilitating adaptive learning experiences that support continual skill development. Furthermore, ML contributes to operational efficiency through automated staffing optimization and administrative task automation, 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. Conclusions ML in nursing offers transformative potential across patient care, education, and operational efficiency, 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, ensuring advancements improve patient outcomes and support nursing professionals without compromising core nursing values.
Keywords: machine learning, artificial intelligence, Digital Health, predictive analytics, ethical considerations, interdisciplinary collaboration Reviewer 5: Response 1 Commented [d2]: Reviewer 5: Comment 1 Commented [d3]: Reviewer 5: comment 2 Commented [d4]: Reviewer 5: Comment 1 Commented [d5]: Reviewer 5: Comment 1
Received: 24 Oct 2024; Accepted: 28 Apr 2025.
Copyright: © 2025 So, Yan, Oi, Chan, Ng, Anders and LAM. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Simon Ching LAM, School of Nursing, Tung Wah College, Kowloon, Hong Kong, SAR China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.