The DOI matches its Crossref record; 114 of the 115 references it cites were found in a registry or an index, and 1 are not checked yet.
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.
Overview
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
Verification
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?
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
“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”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · cited 1×
“However, healthcare data often suffers from inconsistencies, incompleteness, and lack of standardization across different systems and institutions (39).”
Precision medicine, AI, and the future of personalized health care
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854
Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,676
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395
Blockchain distributed ledger technologies for biomedical and health care applications
Kuo, T.-T., Kim, H.-E., & Ohno-Machado, L. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx068 · Cited by 1,350
The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century
Varnosfaderani, S. M., & Forouzanfar, M. (2024) · Bioengineering · doi:10.3390/bioengineering11040337 · Cited by 1,141
Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
A governance model for the application of AI in health care
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979
Clinical decision support in the era of artificial intelligence
Shortliffe, E. H., & Sepúlveda, M. J. (2018) · JAMA · doi:10.1001/jama.2018.17163 · Cited by 920
The ethics of artificial intelligence
Bostrom, N., & Yudkowsky, E. (2014) · The Cambridge Handbook of Artificial Intelligence · doi:10.1017/cbo9781139046855.020 · Cited by 918
An interpretable machine learning model for accurate prediction of sepsis in the ICU
Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002936 · Cited by 832
Precision Medicine: From Science To Value
Ginsburg, G. S., & Phillips, K. A. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.1624 · Cited by 817
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2018-000798 · Cited by 796
Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638
What do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636
Scoping review on the use of socially assistive robot technology in elderly care
Abdi, J., Al-Hindawi, A., Ng, T., & Vizcaychipi, M. P. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-018815 · Cited by 583
Digital twins to personalize medicine
on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., et al. (2020) · Genome Medicine · doi:10.1186/s13073-019-0701-3 · Cited by 566
Clinical virtual simulation in nursing education: randomized controlled trial
Padilha, J. M., Machado, P. P., Ribeiro, A., Ramos, J., & Costa, P. (2019) · Journal of Medical Internet Research · doi:10.2196/11529 · Cited by 550
Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis
Choudhury, A., & Shamszare, H. (2023) · Journal of Medical Internet Research · doi:10.2196/47184 · Cited by 470
Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., et al. (2021) · Journal of Advanced Nursing · doi:10.1111/jan.14855 · Cited by 453
Holmes, T. C., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00700-y · Cited by 413
Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106649 · Cited by 407
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00852-5 · Cited by 404
The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries
Guo, J., & Li, B. (2018) · Health Equity · doi:10.1089/heq.2018.0037 · Cited by 401
The emerging clinical role of wearables: Factors for successful implementation in healthcare
Smuck, M., Odonkor, C. A., Wilt, J. K., Schmidt, N., & Swiernik, M. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00418-3 · Cited by 393
Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/12887 · Cited by 384
The potential of artificial intelligence to improve patient safety: a scoping review
Bates, D. W., Levine, D. M., Syrowatka, A., Kuznetsova, M., Craig, K. J. T., Rui, A., Jackson, G. P., & Rhee, K. B. L. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00423-6 · Cited by 377
How the nursing profession should adapt for a digital future
Booth, R. G., Strudwick, G., McBride, S. G., O’Connor, S., & López, A. L. S. (2021) · BMJ · doi:10.1136/bmj.n1190 · Cited by 369
Will artificial intelligence solve the human resource crisis in healthcare?
Meskó, B., Hetényi, G., & Győrffy, Z. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3359-4 · Cited by 360
Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence
von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., et al. (2022) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.104153 · Cited by 360
Fernandes, M. P., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S. N., & Sousa, J. M. C. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101762 · Cited by 341
Artificial intelligence in healthcare and education
Dave, M., & Patel, N. (2023) · British Dental Journal · doi:10.1038/s41415-023-5845-2 · Cited by 340
Han, R., Acosta, J. N., Shakeri, Z., Ioannidis, J. P. A., Topol, E. J., & Rajpurkar, P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00047-5 · Cited by 336 · cited 1×
“To address this limitation, future work should focus on rigorous, multi-site clinical trials that evaluate the sustained effects of AI on patient outcomes, nursing efficiency, and healthcare costs (61).”
Karimian, G., Petelos, E., & Evers, S. (2022) · AI and Ethics · doi:10.1007/s43681-021-00131-7 · Cited by 328
Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration
Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020) · New England Journal of Medicine · doi:10.1056/nejmsa2001090 · Cited by 314
Nurse staffing levels and patient outcomes: a systematic review of longitudinal studies.
Dall'Ora, C., Saville, C., Rubbo, B., Turner, L., Jones, J., & Griffiths, P. (2022) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2022.104311 · Cited by 314
Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
Ding, X., Clifton, D., Ji, N., Lovell, N. H., Bonato, P., Chen, W., Yu, X., Xue, Z., Xiang, T., Long, X., et al. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2992838 · Cited by 281
Krick, T., Huter, K., Domhoff, D., Schmidt, A., Rothgang, H., & Wolf-Ostermann, K. (2019) · BMC Health Services Research · doi:10.1186/s12913-019-4238-3 · Cited by 279
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278
Designing fair AI for managing employees in organizations: a review, critique, and design agenda
Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R. (2020) · Human–Computer Interaction · doi:10.1080/07370024.2020.1735391 · Cited by 268
The three ghosts of medical AI: Can the black-box present deliver?
Quinn, T. P., Jacobs, S., Senadeera, M., Le, V., & Coghlan, S. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102158 · Cited by 246
Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2020) · JMIR Nursing · doi:10.2196/23939 · Cited by 238
Ethics of healthcare robotics: Towards responsible research and innovation
Stahl, B. C., & Coeckelbergh, M. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.08.018 · Cited by 235
Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications
Schönberger, D. (2019) · International Journal of Law and Information Technology · doi:10.1093/ijlit/eaz004 · Cited by 230
Opening the black box of AI‐Medicine
Poon, A. I. F., & Sung, J. J. �. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15384 · Cited by 223
Healthcare scheduling in optimization context: a review
Abdalkareem, Z. A., Amir, A., Al‐Betar, M. A., Ehkhan, P., & Hammouri, A. I. (2021) · Health and Technology · doi:10.1007/s12553-021-00547-5 · Cited by 222
Can nurses remain relevant in a technologically advanced future?
Pepito, J. A., & Locsin, R. (2019) · International Journal of Nursing Sciences · doi:10.1016/j.ijnss.2018.09.013 · Cited by 221 · cited 1×
“Similarly, AI-enabled care planning systems could assist nurses in formulating and continuously optimizing individualized care strategies, drawing insights from extensive patient data, evidence-based guidelines, and best practices (3).”
Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., Mertens, M., Ting, D. S. W., Ong, J. C. L., Liu, M., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00143-2 · Cited by 212
Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future
Rony, M. K. K., Parvin, M. R., & Ferdousi, S. (2024) · Nursing Open · doi:10.1002/nop2.2070 · Cited by 202
Bayesian networks in healthcare: Distribution by medical condition
McLachlan, S., Dube, K., Hitman, G. A., Fenton, N., & Kyrimi, E. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101912 · Cited by 201
Stokes, F., & Palmer, A. (2020) · Nursing Philosophy · doi:10.1111/nup.12306 · Cited by 200
Artificial Intelligence for healthcare in africa
Owoyemi, A., Owoyemi, J., Osiyemi, A., & Boyd, A. (2020) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.00006 · Cited by 199
Babel, A., Taneja, R., Mondello Malvestiti, F., Monaco, A., & Donde, S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.669869 · Cited by 199
Ye, J. (2020) · JMIR Medical Informatics · doi:10.2196/19866 · Cited by 194
The state of artificial intelligence in nursing education: past, present, and future directions
De Gagne, J. C. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20064884 · Cited by 191
Rony, M. K. K., Kayesh, I., Bala, S. D., Akter, F., & Parvin, M. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25718 · Cited by 190
Integration of artificial intelligence into nursing practice
Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022) · Health and Technology · doi:10.1007/s12553-022-00697-0 · Cited by 172
Chang, Y. M., & Lai, C.-L. (2020) · Nurse Education Today · doi:10.1016/j.nedt.2020.104670 · Cited by 151
Applications of Artificial Intelligence in Nursing Care: A Systematic Review
Martinez‐Ortigosa, A., Martinez-Granados, A., Gil-Hernández, E., Rodríguez-Arrastia, M. J., Ropero‐Padilla, C., & Román, P. (2023) · Journal of Nursing Management · doi:10.1155/2023/3219127 · Cited by 151
Irani, Z., Abril, R. M., Weerakkody, V., Omar, A., & Sivarajah, U. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101784 · Cited by 133
Security challenges and solutions using healthcare cloud computing
Mehrtak, M., SeyedAlinaghi, S., MohsseniPour, M., Noori, T., Karimi, A., Shamsabadi, A., Heydari, M., Barzegary, A., Mirzapour, P., Soleymanzadeh, M., et al. (2021) · Journal of Medicine and Life · doi:10.25122/jml-2021-0100 · Cited by 131
Foronda, C. L., Alfes, C. M., Dev, P., Kleinheksel, A. J., Nelson, D. A., O'Donnell, J. M., & Samosky, J. T. (2016) · Nurse Educator · doi:10.1097/nne.0000000000000295 · Cited by 129
Using Artificial Intelligence in Infection Prevention
Fitzpatrick, F., Doherty, A., & Lacey, G. (2020) · Current treatment options in infectious diseases/Current treatment options in infectious disease · doi:10.1007/s40506-020-00216-7 · Cited by 119
Artificial Intelligence in Nursing Education: Opportunities and Challenges
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023) · Hawai‘i Journal of Health & Social Welfare · doi:10.62547/yeis2105 · Cited by 114
A Review of Natural Language Processing in Medical Education
Chary, M. A., Parikh, S., Manini, A. F., Boyer, E. W., & Radeous, M. (2018) · Western Journal of Emergency Medicine · doi:10.5811/westjem.2018.11.39725 · Cited by 112
ARI: the Social Assistive Robot and Companion
Cooper, S., Fava, A. D., Vivas, C., Marchionni, L., & Ferro, F. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223470 · Cited by 112
AI in healthcare: navigating opportunities and challenges in digital communication
Sun, G., & Zhou, Y. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1291132 · Cited by 105
Artificial intelligence in nursing: Current trends, possibilities and pitfalls
Ahmed, S. K. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100072 · Cited by 104
The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records
Assale, M., Dui, L. G., Cina, A., Seveso, A., & Cabitza, F. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00066 · Cited by 100
Personalized and Adaptive Learning
Taylor, D. L., Yeung, M., & Bashet, A. (2021) · Innovative Learning Environments in STEM Higher Education · doi:10.1007/978-3-030-58948-6_2 · Cited by 100
Interdisciplinary collaboration between nursing and engineering in health care: A scoping review
Zhou, Y. M., Li, Z., & Li, Y. (2021) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.103900 · Cited by 96
Cresswell, K., Callaghan, M., Khan, S., Sheikh, Z., Mozaffar, H., & Sheikh, A. (2020) · Health Informatics Journal · doi:10.1177/1460458219900452 · Cited by 87
Radanliev, P., & Roure, D. D. (2022) · Health and Technology · doi:10.1007/s12553-022-00691-6 · Cited by 85
The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings
Pailaha, A. D. (2023) · SAGE Open Nursing · doi:10.1177/23779608231196847 · Cited by 85
Golden, G. M., Popescu, C., Israel, S. A., Perlman, K., Armstrong, C., Fratila, R., Tanguay-Sela, M., & Benrimoh, D. A. (2024) · Health Policy and Technology · doi:10.1016/j.hlpt.2024.100844 · Cited by 71
Intelligent humanoid robots expressing artificial humanlike empathy in nursing situations
Pepito, J. A., Ito, H., Betriana, F., Tanioka, T., & Locsin, R. C. (2020) · Nursing Philosophy · doi:10.1111/nup.12318 · Cited by 69
Data to knowledge to improvement: creating the learning health system
McDonald, P. L., Foley, T., Verheij, R. A., Braithwaite, J., Rubin, J. C., Harwood, K., Phillips, J. R., Gilman, S. D., & van der Wees, P. J. (2024) · BMJ · doi:10.1136/bmj-2023-076175 · Cited by 65
Interdisciplinary research: shaping the healthcare of the future
Smye, S. W., & Frangi, A. F. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0025 · Cited by 64
Koutentakis, D., Pilozzi, A. R., & Huang, X. (2020) · Healthcare · doi:10.3390/healthcare8020073 · Cited by 62
Machine learning in patient flow: a review
El-Bouri, R., Taylor, T., Youssef, A., Zhu, T., & Clifton, D. A. (2021) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/abddc5 · Cited by 60
Artificial Intelligence and Robotics: A Nurse Leader's Primer
Clipper, B., Batcheller, J. A., Thomaz, A. L., & Rozga, A. (2018) · Nurse Leader · doi:10.1016/j.mnl.2018.07.015 · Cited by 57
Incivility Among Intensive Care Nurses
Nikstaitis, T., & Simko, L. C. (2014) · Dimensions of Critical Care Nursing · doi:10.1097/dcc.0000000000000061 · Cited by 52
From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods
Smith, M., Sattler, A., Hong, G., & Lin, S. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06394-w · Cited by 51
Combining machine learning and optimization for the operational patient-bed assignment problem
Schäfer, F., Walther, M., Grimm, D. G., & Hübner, A. (2023) · Health Care Management Science · doi:10.1007/s10729-023-09652-5 · Cited by 50
Optimizing human-centered AI for healthcare in the Global South
Okolo, C. T. (2022) · Patterns · doi:10.1016/j.patter.2021.100421 · Cited by 49
Nurses’ Use of Clinical Decision Support
Piscotty, R., & Kalisch, B. J. (2014) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000110 · Cited by 45
Factors governing the adoption of artificial intelligence in healthcare providers
Davenport, T. H., & GLASER, J. P. (2022) · Discover Health Systems · doi:10.1007/s44250-022-00004-8 · Cited by 42
Virtual Clinical Education: Going the Full Distance in Nursing Education
Dutile, C., Wright, N. A., & Beauchesne, M. A. (2011) · Newborn and Infant Nursing Reviews · doi:10.1053/j.nainr.2010.12.008 · Cited by 41
Varma, J. R., Fernando, S., Ting, B. Y., Aamir, S., & Sivaprakasam, R. (2023) · Cureus · doi:10.7759/cureus.39701 · Cited by 41
Keim‐Malpass, J., & Moorman, L. P. (2021) · International Journal of Nursing Studies Advances · doi:10.1016/j.ijnsa.2021.100019 · Cited by 36
Features of Computerized Clinical Decision Support Systems Supportive of Nursing Practice
Lee, S. (2013) · CIN Computers Informatics Nursing · doi:10.1097/01.ncn.0000432127.99644.25 · Cited by 32
Blending Adaptive Learning Technology Into Nursing Education: A Scoping Review
Andersen, B. L., Jørnø, R. L. V., & Nortvig, A.-M. (2021) · Contemporary Educational Technology · doi:10.30935/cedtech/11370 · Cited by 28
Systematic review and meta-analysis for a Global Patient co-Owned Cloud (GPOC)
Lidströmer, N., Davids, J., El-Sharkawy, M., Ashrafian, H., & Herlenius, E. (2024) · Nature Communications · doi:10.1038/s41467-024-46503-5 · Cited by 28
Contributions of Artificial Intelligence to Decision Making in Nursing: A Scoping Review Protocol
Fernandes, F., Santos, P., de Sá, L. O., & Neves, J. (2023) · Nursing Reports · doi:10.3390/nursrep13010007 · Cited by 26
Artificial intelligence for global health: cautious optimism with safeguards
Pujari, S., Reis, A. A., Zhao, Y., Alsalamah, S., Serhan, F., Reeder, J. C., & Labrique, A. B. (2023) · Bulletin of the World Health Organization · doi:10.2471/blt.23.290215 · Cited by 24
Vaismoradi, M., Rae, J., Turunen, H. E., & Logan, P. (2024) · Digital Health · doi:10.1177/20552076241287272 · Cited by 21
The Use of Virtual Reality Simulations in Nursing Education, and Patient Safety
Bayram, S., & Çalışkan, N. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.94108 · Cited by 21
Exploring the use of high-fidelity simulation training to enhance clinical skills
Kirkham, L. (2018) · Nursing Standard · doi:10.7748/ns.2018.e10693 · Cited by 21
Hatem, N. A. H. (2024) · Integrated Pharmacy Research and Practice · doi:10.2147/iprp.s466748 · Cited by 20
Effect of Mixed Variation of Magnesium Carbonate and Bottom Ash for Stabilization of Subgrade Soils
Firdaus, N. A. A., Ismanti, S., & Rifa'i, A. (2024) · The Open Civil Engineering Journal · doi:10.2174/0118749445304699240416074458 · Cited by 20
Li, X., Zhou, H.-P., Zhou, Z., Du, N., Zhong, E., Zhai, K., Liu, N., & Zhou, L. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001529 · Cited by 18
A summit on a Global Patient co-Owned Cloud (GPOC)
Lidströmer, N., Davids, J., El-Sharkawy, M., Ashrafian, H., & Herlenius, E. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00112-w · Cited by 17
Risk profiling in the prevention and treatment of chronic wounds using artificial intelligence
Cross, K., & Harding, K. G. (2022) · International Wound Journal · doi:10.1111/iwj.13952 · Cited by 17
Arnold, D. R., King, J. S., Fulton, B. D., Montague, A. D., Gudiksen, K. L., Greaney, T. L., & Scheffler, R. M. (2024) · Health Services Research · doi:10.1111/1475-6773.14291 · Cited by 12
Augmented Intelligence and Nursing
Skiba, E. (2017) · Nursing Education Perspectives · doi:10.1097/01.nep.0000000000000124 · Cited by 11
Cybersecurity of AI medical devices: risks, legislation, and challenges
Biasin, E., Kamenjašević, E., & Ludvigsen, K. R. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781802205657.ch04 · Cited by 10
Artificial Intelligence and Machine Learning to Accelerate Translational Research
Affairs, P. A. G., & National Academies of Sciences, E. (2018) · National Academies Press eBooks · doi:10.17226/25197 · Cited by 9
Role of Artificial Intelligence in Clinical and Hospital Pharmacy
Sridhar, S. B., Karattuthodi, M. S., & Parakkal, S. A. (2024) · Nutritional neurosciences · doi:10.1007/978-981-97-2577-9_12 · Cited by 5
Threat Artificial Intelligence and Cyber Security in Health Care Institutions
Fernandes, A., Figueiredo, M., Carvalho, F., Neves, J., & Vicente, H. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72236-4_13 · Cited by 3
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
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
