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The integration of AI in nursing: addressing current applications, challenges, and future directions
Open accessAbstract
Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.
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In the citing paper
“Mobile health and virtual care tools can enhance nursing workflows, improve clinical decision making, and help ensure that patients in under-resourced areas experience fewer gaps in care and more coordinated health services.4,5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This absence limits evaluation and improvement of AI tools and hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.4,5,7”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while agentic AI may generate inaccurate, inequitable, or unsafe recommendations for marginalized populations.5,21,22”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Without diverse representative and contextually relevant training data, empathic AI systems may misinterpret emotional cues across different cultural, linguistic or socioeconomic groups”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Mobile health and virtual care tools can enhance nursing workflows, improve clinical decision making, and help ensure that patients in under-resourced areas experience fewer gaps in care and more coordinated health services.4,5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Mobile health and virtual care tools can enhance nursing workflows, improve clinical decision making, and help ensure that patients in under-resourced areas experience fewer gaps in care and more coordinated health services.4,5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This absence limits evaluation and improvement of AI tools and hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.4,5,7”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In real-world practice, our human-centered AI framework could help alleviate documentation burden, reduce cognitive overload, and enhance workflow efficiency across diverse cancer care settings and host communities.5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Overreliance on automated interactions or AI-mediated communication could degrade patient–clinician relationships.5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In real-world practice, our human-centered AI framework could help alleviate documentation burden, reduce cognitive overload, and enhance workflow efficiency across diverse cancer care settings and host communities.5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In real-world practice, our human-centered AI framework could help alleviate documentation burden, reduce cognitive overload, and enhance workflow efficiency across diverse cancer care settings and host communities.5”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [5]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Equally concerning is the issue of algorithmic bias and inequitable system behavior.”
The cited source says what the claim attributes to it.
In the citing paper
“Currently, there is a lack of nursing-specific AI frameworks designed to align emerging technologies with ethical nursing principles such as empathy, advocacy, and patient-centered care.4,5”
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Citations
Cites
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Cited by
Sorted by citations
El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1681784 · Cited by 62
Alruwaili, A. N., Alshammari, A. M., Alhaiti, A., Elsharkawy, N. B., Ali, S. I., & Ramadan, O. M. E. (2025) · BMC Nursing · doi:10.1186/s12912-025-03044-6 · Cited by 60
Boztepe, H., Akdeniz Kudubeş, A., Semerci Şahin, R., Durmuş Sarıkahya, S., & Çınar Özbay, S. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104568 · Cited by 38
Zeng, Q., Huang, X., Zhu, J., Su, S., Hu, Y., & Zhang, X. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1622802 · Cited by 42
Exploring the ethical dimensions of AI integration in nursing practice: A systematic review
Alnawafleh, K. A., Almagharbeh, W. T., Alfanash, H. A., Alasmari, A. A., Alharbi, A. A., Alamrani, M. H., Alkubati, S. A., Altayar, M. A., & Rezq, K. A. (2025) · Journal of Nursing Regulation · doi:10.1016/j.jnr.2025.08.001 · Cited by 32
Metadata
- Authors
- Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W.
- Affiliations
- First Affiliated Hospital of GuangXi Medical University (CN)
- Corresponding author
- Pan, Songcheng
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2025-02-11
- Volume
- 12
- Pages
- 1545420-1545420
- ISSN
- 2296-858X (ISSN-L)
- PMC
- PMC11850350
- OpenAlex
- W4407382566
- Cited by
- 143
- References
- 114
- Citations by year
- 2026: 89 · 2025: 53
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in nursing, clinical decision support systems, patient monitoring, nursing education, implementation barriers, AI ethics, workforce adaptation, individualized care, global health, patient outcomes
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