Nursing in the Artificial Intelligence (AI) Era: Optimizing Staffing for Tomorrow
Open accessAbstract
Amid the global nursing shortage, artificial intelligence (AI) offers a solution to align patient needs with nursing expertise, ensuring equitable workload distribution. Highlighting the transformative potential of artificial intelligence in healthcare, this editorial underscores its revolutionary impact on nursing staffing. By leveraging AI, we can enhance patient outcomes and operational efficiency and reduce staff burnout. However, challenges like data security and job stability arise. It is pivotal to emphasize its optimal integration, engaging nurses in decision-making, rigorous training, and prioritizing data security. This holistic approach ensures that contemporary healthcare practices benefit from AI's capabilities while upholding core values.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Pediatric nursing care, with its ethical and professional complexities, stands to gain significantly from the strategic incorporation of AI.
“Pediatric nursing care, with its ethical and professional complexities, stands to gain significantly from the strategic incorporation of AI.”
From The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care (2024) · cites this paper as [14]
The Editorial says strategic AI integration could improve nursing staffing and patient outcomes, while requiring ethical guidelines, training, and attention to nurses’ roles. It does not discuss pediatric nursing or its particular ethical and professional complexities, so the claim extends the paper’s general argument to a population it does not address. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global nurse shortages—the facts, the impact and action for change
Drennan, V. M., & Ross, F. (2019) · British Medical Bulletin · doi:10.1093/bmb/ldz014 · Cited by 493 · see citing works
Artificial intelligence: opportunities and risks for public health
Panch, T., Pearson‐Stuttard, J., Greaves, F., & Atun, R. A. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30002-0 · Cited by 260 · see citing works
Artificial Intelligence and Nursing: The Future Is Now
Clancy, T. R. (2020) · JONA: The Journal of Nursing Administration · doi:10.1097/nna.0000000000000855 · Cited by 103 · see citing works
An ethically mindful approach to AI for health care
Morley, J., & Floridi, L. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32975-7 · Cited by 69 · see citing works
AI in medicine: creating a safe and equitable future
Lancet, T. (2023) · The Lancet · doi:10.1016/s0140-6736(23)01668-9 · Cited by 55 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial Intelligence in the Organization of Nursing Care: A Scoping Review
Silva, J. M. A. V., Martins, M. M., Trindade, L. D. L., Faria, A. D. C. A., Pereira, S. C. D. A., Züge, S. S., & Ribeiro, O. M. P. L. (2024) · Nursing Reports · doi:10.3390/nursrep14040202 · Cited by 75 · see citing works
Abujaber, A. A., Abd-alrazaq, A., Al-Qudimat, A. R., & Nashwan, A. J. (2023) · Cureus · doi:10.7759/cureus.48643 · Cited by 91 · see citing works
Self-citationShin, H., De Gagne, J. C., Kim, S. S., & Hong, M. (2024) · CIN: Computers, Informatics, Nursing · doi:10.1097/cin.0000000000001177 · Cited by 47 · see citing works
Berghea, C. E., Ionescu, M. D., Gheorghiu, R. M., Ţincu, I. F., Cobilinschi, C. O., Craiu, M., Bălgrădean, M., & Berghea, F. (2024) · Children · doi:10.3390/children11020240 · Cited by 41 · see citing works
Artificial intelligence in critical care nursing: A scoping review
Park, Y., Chang, S. J., & Kim, E. (2025) · Australian Critical Care · doi:10.1016/j.aucc.2025.101225 · Cited by 31 · see citing works
Ünal, A. S., & Avcı, A. (2024) · Journal of Pediatric Nursing · doi:10.1016/j.pedn.2024.09.012 · Cited by 33 · see citing works
Ibrahim, A. M., Zoromba, M. A., Abousoliman, A. D., Zaghamir, D. E. F., Alenezi, I. N., Elsayed, E. A. H., & Mohamed, H. A. H. (2025) · BMC Nursing · doi:10.1186/s12912-025-02798-3 · Cited by 36 · see citing works
Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells
Isavand, P., Aghamiri, S. S., & Amin, R. (2024) · Biomedicines · doi:10.3390/biomedicines12081753 · Cited by 24 · see citing works
Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence
Abukhadijah, H. J., & Nashwan, A. J. (2024) · Global Journal on Quality and Safety in Healthcare · doi:10.36401/jqsh-24-4 · Cited by 24 · see citing works
Self-citationMudd, A., Conroy, T., Voldbjerg, S. L., Goldschmied, A. Z., Feo, R., & Schuwirth, L. (2025) · Journal of Clinical Nursing · doi:10.1111/jocn.17818 · Cited by 7 · see citing works
Pang, X., Pan, J., Chen, Z., Cao, X., Shi, J., & Mao, L. (2025) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2025.105207 · Cited by 4 · see citing works
Othman, M. I., Nashwan, A. J., & Abujaber, A. A. (2025) · Nursing Open · doi:10.1002/nop2.70195 · Cited by 4 · see citing works
Self-citationEmpowering Care: Transforming Nursing Through Artificial Intelligence
Lukkahatai, N., Diño, M. J. S., & Saligan, L. N. (2025) · Artificial intelligence · doi:10.5772/intechopen.1010323 · Cited by 4 · see citing works
Rezaeikhonakdar, D., Wrigley, J., & Shaw, R. J. (2024) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001131 · Cited by 1 · see citing works
Tock, R., & Piron, C. (2026) · Soins Cadres · doi:10.1016/j.scad.2026.03.008
- Authors
- Nashwan, A. J., & Abujaber, A. A.
- Affiliations
- Hamad Medical Corporation (QA)
- Corresponding author
- Nashwan, Abdulqadir J
- Venue
- Cureus
- Published
- 2023-10-18
- Volume
- 15
- Issue
- 10
- Pages
- e47275-e47275
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10655932
- OpenAlex
- W4387737070
- Cited by
- 15
- References
- 5
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 7 · 2023: 1
- 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
- Health Sciences › Nursing › Research and Theory › Nursing education and management
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- artificial intelligence in nursing, nurse staffing, nursing shortage, workload distribution, staff burnout, data security, patient outcomes, operational efficiency
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 ›
Are you an author, or do you see a problem with this page? Request a review
