Can Artificial Intelligence Replace the Unique Nursing Role?
Open accessAbstract
Artificial intelligence (AI) is transforming healthcare, offering potential benefits and challenges. In healthcare, AI enhances efficiency, streamlines processes, and supports decision-making. However, challenges include potential errors and biases in algorithms, data privacy concerns, legal and ethical issues, and resistance to change. In nursing, a delicate balance emerges between AI and human touch. While AI aids in data-driven decision-making and administrative tasks, it lacks the emotional intelligence, empathy, and nuanced understanding crucial to nursing care. Nurses excel in critical thinking, adaptability to dynamic situations, patient advocacy, collaboration, and establishing human connections. AI supports these functions by automating routine tasks and offering decision support tools, yet its rigidity in dynamic situations and lack of human touch pose limitations. This review underscores the necessity of careful AI integration in healthcare, acknowledging its advantages while mitigating drawbacks. In nursing, the symbiosis between AI and human qualities is vital. The role of AI should be to complement, not replace, the unique skills and empathetic aspects of nursing care. Addressing concerns related to bias, transparency, data privacy, and professional training is essential for maximizing the benefits of AI in healthcare while preserving the human touch in patient care. This article explores whether AI can replace unique nursing roles.
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AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits.
“For example, AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits, detect early indicators of anxiety or depression, and escalate concerns so nurses can intervene proactively and efficiently.13,14”
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 [14]
The Review (page unavailable) says AI-powered virtual assistants can provide continuous patient support, including reminders and monitoring. It does not specify that they autonomously initiate contact between clinical visits. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAI-powered chatbots or virtual assistants can escalate concerns so nurses can intervene proactively and efficiently.
“For example, AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits, detect early indicators of anxiety or depression, and escalate concerns so nurses can intervene proactively and efficiently.13,14”
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 [14]
The Review says AI-powered virtual assistants can monitor patients continuously and that AI can detect changes early, potentially allowing early intervention. It does not say that chatbots escalate concerns to nurses, or describe this workflow for anxiety or depression. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAI-powered chatbots or virtual assistants can detect early indicators of anxiety or depression.
“For example, AI-powered chatbots or virtual assistants can reach out to patients autonomously between clinical visits, detect early indicators of anxiety or depression, and escalate concerns so nurses can intervene proactively and efficiently.13,14”
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 [14]
The Review (page unavailable) says AI-powered virtual assistants can monitor patients and that AI algorithms can detect subtle changes in patient conditions early. It does not say that chatbots or virtual assistants detect early indicators of anxiety or depression specifically. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe 18 included studies collectively explored diverse applications of AI in nursing across research, practice, and education.
“The 18 included studies in this umbrella review collectively explored the diverse applications of AI in nursing across research, practice, and education [32-49].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [40]
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Fernández, R. C., & Solórzano, M. (2026) · Applied Artificial Intelligence · doi:10.1080/08839514.2026.2726917
Wan, W., Yu, X., Guo, Q., Zhang, Z., Huang, M., Liu, G., & Zhou, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1916153
- Authors
- Mohanasundari, S. K., Kalpana, M., Madhusudhan, U., Vasanthkumar, K., B, R., Singh, R., Vashishtha, N., & Bhatia, V.
- Affiliations
- All India Institute of Medical Sciences Bibinagar (IN)
- Corresponding author
- Bhatia, Vikas
- Venue
- Cureus
- Published
- 2023-12-27
- Volume
- 15
- Issue
- 12
- Pages
- e51150-e51150
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10811613
- OpenAlex
- W4390269764
- Cited by
- 71
- References
- 29
- Citations by year
- 2026: 21 · 2025: 42 · 2024: 8
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › General Health Professions › Nursing Roles and Practices
- Keywords
- artificial intelligence in nursing, nursing role, nurse empathy, algorithmic bias, data privacy, AI decision support, nursing education, human-AI collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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