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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Mobile health and virtual care tools can help ensure that patients in under-resourced areas experience fewer gaps in care and more coordinated health services.
“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]
Section 2.3 (page unavailable) says virtual nursing assistants can improve access to nursing services, and section 4.8 discusses developing AI nursing tools for underserved, resource-limited communities. The paper does not establish that mobile health and virtual care tools reduce gaps in care or make services more coordinated in those areas. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe absence of nursing-specific AI frameworks hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.
“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]
Section 4.4 says nursing-tailored ethical frameworks are needed, and sections 4.1 and 4.5 say insufficient longitudinal research limits understanding of AI’s long-term effects on nursing practice and patient care. The paper does not say that the absence of nursing-specific frameworks causes that lack of insight or limits evaluation and improvement of AI tools; it treats frameworks and long-term studies as distinct needs. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWithout diverse representative and contextually relevant training data, agentic AI may generate inaccurate, inequitable, or unsafe recommendations for marginalized populations.
“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]
Section 3.1 says AI needs diverse, high-quality datasets to function effectively and avoid bias; section 3.2 says biased training data may produce discriminatory outcomes and worsen health disparities. Section 4.8 also calls for AI adapted to diverse populations and contexts. The paper does not specifically address agentic AI or establish that it generates unsafe recommendations for marginalized populations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredWithout diverse representative and contextually relevant training data, empathic AI systems may misinterpret emotional cues across different cultural, linguistic or socioeconomic groups.
“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]
Section 3.1, “Technical challenges,” says AI models need large, diverse, high-quality datasets to function effectively and avoid bias. Section 4.8, “Cultural and contextual adaptability,” calls for AI suited to diverse cultural contexts. Neither section addresses empathic AI misinterpreting emotional cues or identifies linguistic and socioeconomic groups as contexts for that risk. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMobile health and virtual care tools can enhance nursing workflows.
“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]
Section 2.3 (Administrative and workflow optimization; page unavailable) says virtual nursing assistants handle routine inquiries, freeing nurses for more complex tasks. The Introduction also says nursing-specific AI tools can enhance workflows. The cited passages do not specifically establish that mobile health tools enhance nursing workflows. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Mobile health and virtual care tools can improve clinical decision making.
“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]
Section 2.1 says AI-powered clinical decision support systems help nurses make more informed care decisions. Sections 2.2 and 2.3 discuss wearable monitoring and virtual nursing assistants, but do not say that mobile health or virtual care tools themselves improve clinical decision-making. The source supports the decision-support point, not the claim's specific attribution to those tools. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe absence of nursing-specific AI frameworks limits evaluation and improvement of AI tools.
“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]
Section 4.4 identifies a need for ethical frameworks tailored to nursing, and section 4.1 says limited clinical evidence restricts assessment of AI’s long-term impacts. The paper does not say that the absence of nursing-specific frameworks itself limits evaluation or improvement of AI tools; it attributes the assessment gap to insufficient large-scale, longitudinal studies. No page numbers are available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe human-centered AI framework could help reduce cognitive overload across diverse cancer care settings and host communities.
“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]
Section 2.3, “Administrative and workflow optimization” (page unavailable), says AI can reduce nursing documentation burden and improve workflow efficiency. The cited paper does not establish that the citing authors’ framework reduces cognitive overload, or that these benefits extend across cancer care settings and host communities; section 4.8 instead calls for further study of adaptation to diverse contexts. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOverreliance on automated interactions or AI-mediated communication could degrade patient-clinician relationships.
“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]
Section 3.2 (Ethical considerations) says over-reliance on AI could diminish the nurse–patient relationship. Section 2.3 describes patient-facing chatbots, but the paper does not specifically link automated communication to that risk or extend the finding to patient–clinician relationships beyond nursing. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe human-centered AI framework could help alleviate documentation burden across diverse cancer care settings and host communities.
“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]
Section 2.3 (Administrative and workflow optimization; page unavailable) says AI can reduce nursing administrative burdens and improve workflow efficiency, including by automating documentation. It does not assess the citing authors’ human-centered framework or establish that it would work across cancer care settings and host communities; the cited passages also do not substantiate reduced cognitive overload. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe human-centered AI framework could help enhance workflow efficiency across diverse cancer care settings and host communities.
“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]
Section 2.3, “Administrative and workflow optimization” (page unavailable), says AI applications in nursing can reduce administrative burdens and improve workflow efficiency. Sections 3.5 and 4.8 discuss user-centric integration and adapting AI to diverse healthcare contexts. The paper does not establish that the citing authors’ particular human-centered framework would improve efficiency in cancer care settings or host communities. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlgorithmic bias and inequitable system behavior are issues in AI-enabled care.
“Equally concerning is the issue of algorithmic bias and inequitable system behavior.”
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.
“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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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 22,182 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,501 · see citing works
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 · see citing works
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 · see citing works
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 1,069 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The ethics of artificial intelligence
Bostrom, N., & Yudkowsky, E. (2014) · The Cambridge Handbook of Artificial Intelligence · doi:10.1017/cbo9781139046855.020 · Cited by 918 · see citing works
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 · see citing works
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2018-000798 · Cited by 796 · see citing works
Precision Medicine: From Science To Value
Ginsburg, G. S., & Phillips, K. A. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.1624 · Cited by 767 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Holmes, T. C., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00700-y · Cited by 397 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 351 · see citing works
Artificial intelligence in healthcare and education
Dave, M., & Patel, N. (2023) · British Dental Journal · doi:10.1038/s41415-023-5845-2 · Cited by 340 · see citing works
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× · see citing works
“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).”
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 314 · see citing works
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 · see citing works
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 · see citing works
Karimian, G., Petelos, E., & Evers, S. (2022) · AI and Ethics · doi:10.1007/s43681-021-00131-7 · Cited by 306 · see citing works
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 · see citing works
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 · see citing works
Kiseleva, A., Kotzinos, D., & De Hert, P. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.879603 · Cited by 278 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 222 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Stokes, F., & Palmer, A. (2020) · Nursing Philosophy · doi:10.1111/nup.12306 · Cited by 200 · see citing works
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 · see citing works
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 · see citing works
Ye, J. (2020) · JMIR Medical Informatics · doi:10.2196/19866 · Cited by 194 · see citing works
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 · see citing works
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 · see citing works
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 182 · see citing works
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 · see citing works
Chang, Y. M., & Lai, C.-L. (2020) · Nurse Education Today · doi:10.1016/j.nedt.2020.104670 · Cited by 151 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 105 · see citing works
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 101 · see citing works
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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 · see citing works
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 · see citing works
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Sun, G., & Zhou, Y. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1291132 · Cited by 95 · see citing works
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 94 · see citing works
Cresswell, K., Callaghan, M., Khan, S., Sheikh, Z., Mozaffar, H., & Sheikh, A. (2020) · Health Informatics Journal · doi:10.1177/1460458219900452 · Cited by 87 · see citing works
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 · see citing works
Radanliev, P., & Roure, D. D. (2022) · Health and Technology · doi:10.1007/s12553-022-00691-6 · Cited by 75 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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Smye, S. W., & Frangi, A. F. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2021-0025 · Cited by 64 · see citing works
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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 · see citing works
Koutentakis, D., Pilozzi, A. R., & Huang, X. (2020) · Healthcare · doi:10.3390/healthcare8020073 · Cited by 59 · see citing works
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Clipper, B., Batcheller, J. A., Thomaz, A. L., & Rozga, A. (2018) · Nurse Leader · doi:10.1016/j.mnl.2018.07.015 · Cited by 57 · see citing works
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Nikstaitis, T., & Simko, L. C. (2014) · Dimensions of Critical Care Nursing · doi:10.1097/dcc.0000000000000061 · Cited by 52 · see citing works
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 · see citing works
Optimizing human-centered AI for healthcare in the Global South
Okolo, C. T. (2022) · Patterns · doi:10.1016/j.patter.2021.100421 · Cited by 48 · see citing works
Nurses’ Use of Clinical Decision Support
Piscotty, R., & Kalisch, B. J. (2014) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000110 · Cited by 45 · see citing works
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 43 · see citing works
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 · see citing works
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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 · see citing works
Varma, J. R., Fernando, S., Ting, B. Y., Aamir, S., & Sivaprakasam, R. (2023) · Cureus · doi:10.7759/cureus.39701 · Cited by 41 · see citing works
Keim‐Malpass, J., & Moorman, L. P. (2021) · International Journal of Nursing Studies Advances · doi:10.1016/j.ijnsa.2021.100019 · Cited by 36 · see citing works
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 · see citing works
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 · see citing works
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Lidströmer, N., Davids, J., El-Sharkawy, M., Ashrafian, H., & Herlenius, E. (2024) · Nature Communications · doi:10.1038/s41467-024-46503-5 · Cited by 28 · see citing works
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 · see citing works
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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 · see citing works
Vaismoradi, M., Rae, J., Turunen, H. E., & Logan, P. (2024) · Digital Health · doi:10.1177/20552076241287272 · Cited by 21 · see citing works
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Bayram, S., & Çalışkan, N. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.94108 · Cited by 21 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Hatem, N. A. H. (2024) · Integrated Pharmacy Research and Practice · doi:10.2147/iprp.s466748 · Cited by 17 · see citing works
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 · see citing works
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 16 · see citing works
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 · see citing works
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Skiba, E. (2017) · Nursing Education Perspectives · doi:10.1097/01.nep.0000000000000124 · Cited by 11 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2 · see citing works
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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 · see citing works
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 54 · see citing works
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 · see citing works
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 · see citing works
Exploring the ethical dimensions of AI integration in nursing practice: A systematic review
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Yuan, Y., Fu, J., Leng, L., Wen, Z., Wei, X., Han, D., Hu, X., Liang, Y., Luo, Q., Zhang, X., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1672140 · Cited by 25 · see citing works
Zhao, Y., Yuan, Y., Wen, Z., Leng, L., Shi, L., Hu, X., Xiaoman, W., Zuo, M., Mou, J., Luo, Q., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1648416 · Cited by 27 · see citing works
Ruksakulpiwat, S., Phianhasin, L., Benjasirisan, C., Su, T., Riangkam, C., Thorngthip, S., Aldossary, H. M., Ahmed, B. H., Pongsuwun, K., & Angkaew, N. (2025) · International Nursing Review · doi:10.1111/inr.70080 · Cited by 30 · see citing works
Dai, Q., Li, M., Shi, S., Yang, M., Wang, Z., Liao, J., Li, Z., Liu, J., Deng, J. J., & Tao, L. (2025) · BMC Nursing · doi:10.1186/s12912-025-03748-9 · Cited by 25 · see citing works
Al-Ewaidat, O. A., & Naffaa, M. M. (2025) · Rheumato · doi:10.3390/rheumato5040017 · Cited by 24 · see citing works
Pepito, J., Acaso, N. J., Merioles, R., & Ismael, J. (2025) · JMIR Nursing · doi:10.2196/72674 · Cited by 24 · see citing works
Sağlam, R., & Kalanlar, B. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104530 · Cited by 24 · see citing works
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Liu, C., Fan, H., Kim, M., Zhou, T., Yang, P., Zhao, L., Wang, Y., Che, Z., Liu, C., Li, B., et al. (2026) · Advanced Science · doi:10.1002/advs.202522900 · Cited by 20 · see citing works
Integration of artificial intelligence in nursing education: a cross-national exploration
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Olawade, D. B., David-Olawade, A. C., Rotifa, O. B., & Wada, O. Z. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104511 · Cited by 18 · see citing works
Batran, A., Ayed, A., Aqtam, I. F., Al-Amer, R. M., Othman, E. H., Ejheisheh, M. A., daamsa, H. N. A., Alkhatib, S. G., & Farajallah, M. (2025) · SAGE Open Nursing · doi:10.1177/23779608251376177 · Cited by 12 · see citing works
Pepito, J. A., Babate, F. J., Soriano, G. P., Valdez, G. F. D., Briggs, H., LaPlante, D., Ismael, J. D., & Al‐Jumayile, S. S. (2026) · Nursing Inquiry · doi:10.1111/nin.70102 · Cited by 8 · see citing works
Amer, F. G. M., Alanazi, M. A., Shaban, M., Abdelgawad, M. E., Mohammed, H. H., Hassan, S., Atrous, M. H., Ibrahim, S. S. M., Mohamed, A. H., & Sabek, E. M. (2025) · International Nursing Review · doi:10.1111/inr.70056 · Cited by 10 · see citing works
Benfatah, M., Elazizi, I., Belhaj, H., & Lamiri, A. (2025) · Journal of Nursing Regulation · doi:10.1016/j.jnr.2025.08.004 · Cited by 9 · see citing works
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Silva, L. I. M., Moreno-Pimentel, A. G., Hernández-Martín, M. M., Moreno, G., Maté‐Muñoz, J. L., & Monroy, A. M. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.08.018 · Cited by 6 · see citing works
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Ling, G., Zuo, H., Sun, Y., & Qian, C. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1665160 · Cited by 6 · see citing works
Yuan, X., Zhu, L., Jiang, K., & Chen, J. (2025) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s548275 · Cited by 6 · see citing works
Berşe, S., Akça, K., Dіrgar, E., & Ağar, A. (2025) · International Journal of Nursing Practice · doi:10.1111/ijn.70083 · Cited by 6 · see citing works
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Nashwan, A., & Kunjavara, J. (2026) · Nursing Inquiry · doi:10.1111/nin.70099 · Cited by 6 · see citing works
Sultan, H. M., Sam, B. J., & Pillai, R. R. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.06.010 · Cited by 5 · see citing works
Shankar, R. (2026) · SAGE Open Nursing · doi:10.1177/23779608261457722 · Cited by 6 · see citing works
Khalifa, A., & Hussein, R. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02391-4 · Cited by 5 · see citing works
Huang, C., Xu, J., Qiu, H., & Yue, Y. (2025) · International Journal of Nursing Practice · doi:10.1111/ijn.70060 · Cited by 4 · see citing works
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Çakır, B., Berk, A., Berşe, S., Göv, P., & Güngör, M. İ. (2025) · Global Nursing Insights · doi:10.31125/globnursinsights.1762516 · Cited by 4 · see citing works
Mersal, F. A., Mersal, N. A., Ibrahim, N. M., Elgazzar, S. E., Alanazi, A. M., Alzughaibi, S., Alanazi, E. M., & Elsayed, E. A. H. (2026) · Digital Health · doi:10.1177/20552076261429671 · Cited by 8 · see citing works
Alnazly, E. K., Absy, N., & Absy, N. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1779382 · Cited by 4 · see citing works
Zhao, L., Yan, H., Yang, Y., Li, Y., Li, K., & Wan, F. (2026) · Nurse Educator · doi:10.1097/nne.0000000000002175 · Cited by 5 · see citing works
Chen, X., Ding, W., Wang, X., Wang, Y., Ma, S., & Tao, M. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1885808 · Cited by 5 · see citing works
Zhou, X., Su, G., Zhu, L., & Liu, Y. (2025) · BMC Nursing · doi:10.1186/s12912-025-04102-9 · Cited by 4 · see citing works
Girdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., & Shaban-Nejad, A. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100694 · Cited by 3 · see citing works
Gürkan, D. Y., & Çakmak, R. (2025) · BMC Nursing · doi:10.1186/s12912-025-03947-4 · Cited by 3 · see citing works
Rixon, A., Phelan, A., Ekberg, S., Senyard, J. M., Mashhady, A., Shaw, A., & Birdthistle, N. (2026) · Nurse Education in Practice · doi:10.1016/j.nepr.2026.104716 · Cited by 4 · see citing works
Pepito, J. A., Babate, F. J., Ismael, J. D., Al‐Jumayile, S. S., & Siddique, M. K. B. (2026) · SAGE Open Nursing · doi:10.1177/23779608261433312 · Cited by 4 · see citing works
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Li, J., Zhang, L., Hou, Q., Jiang, S., Shen, L., & Wei, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1710269 · Cited by 3 · see citing works
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Dabkowski, E., Missen, K., Allen, L., Whitehead, D., & Worn, R. (2026) · International Journal for Educational Integrity · doi:10.1007/s40979-026-00220-2 · Cited by 2 · see citing works
Clej, E. E., Fizedean, C. M., Mavrea, A., Ilie, A. C., Bratu, M. L., & Marc, F. (2026) · Healthcare · doi:10.3390/healthcare14142063 · Cited by 2 · see citing works
Fu, C., Li, J., Fan, H., Ren, R., Kefei, C., Zhang, Y., Yu, J., & Li, J. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871524 · Cited by 3 · see citing works
苗书英, Zhang, Z., & Bao, C. (2026) · Journal of Nursing Management · doi:10.1155/jonm/5288231 · Cited by 2 · see citing works
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Ahmed, H., Akber, N., Saleem, M., Ahmed, F., Yasmeen, R., & Ali, L. (2025) · Indus journal of bioscience research. · doi:10.70749/ijbr.v3i2.731 · Cited by 1 · see citing works
Ceylan, H. (2025) · Turkish Journal of Health Science and Life · doi:10.56150/tjhsl.1738995 · Cited by 1 · see citing works
Kalyani, B., Baretto, J. S., Shiralashetti, I., Shiralashetti, I., Kottalagi, I., Chiraladinni, U., Honakeri, S., Chavadannavar, M. P., Deepa, N. R., & Umar, M. (2025) · International Journal of Reproduction Contraception Obstetrics and Gynecology · doi:10.18203/2320-1770.ijrcog20253544 · Cited by 1 · see citing works
Paygozar, R., Tahery, N., & Abnavi, S. D. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08454-3 · Cited by 1 · see citing works
Fang, M., Wu, Q., Liu, J., & Liu, X. (2025) · Nurse Educator · doi:10.1097/nne.0000000000002097 · Cited by 1 · see citing works
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Nascimento, C., Sousa, E., Martins, H., Santos, E., & Ramos, A. (2026) · Psychiatry International · doi:10.3390/psychiatryint7010039 · Cited by 1 · see citing works
Ge, X., Wang, H., Xu, Y., & Zhang, Y. (2026) · BMC Nursing · doi:10.1186/s12912-026-04433-1 · Cited by 1 · see citing works
Niu, Y., Chen, S., Hu, M., & Li, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1758669 · Cited by 1 · see citing works
Clej, E. E., Mavrea, A., Fizedean, C. M., Tănase, A. D., Ilie, A. C., & Tischer, A. A. (2026) · Healthcare · doi:10.3390/healthcare14080996 · Cited by 2 · see citing works
Son, J.-E., & Park, Y. (2026) · Korean Journal of Adult Nursing · doi:10.7475/kjan.2026.0101 · Cited by 1 · see citing works
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Ramadan, O. M. E. (2026) · SAGE Open Nursing · doi:10.1177/23779608261468455 · Cited by 1 · see citing works
Liu, W., Cao, L., Chen, X. W., & Pan, H. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09918-w · Cited by 1 · see citing works
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Hawkins, N., Fagan, A., Coffey, Y., & Dohnt, H. C. (2026) · Journal of Advanced Nursing · doi:10.1111/jan.70728 · Cited by 1 · see citing works
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Salem, M. F. A., Sharaf, A. K., & Younis, A. A. A. (2026) · BMC Nursing · doi:10.1186/s12912-026-05313-4 · Cited by 1 · see citing works
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Kerna, N. A., Olatokun, T. P., Keke, C. O., Okwuokei, E. C., Chigozie, I. K., Fide-Nwoko, F. U., David, C. O., Azi, C. I., Nwafor, B. N., Ngwu, D. C., et al. (2025) · European Journal of Medical and Health Research · doi:10.59324/ejmhr.2025.3(2).21
La irrupción de la IA en torno a la disciplina enfermera: adaptación a las nuevas competencias
Castillo, F. C., & Rosa-Castillo, A. (2025) · Enfermería Clínica · doi:10.1016/j.enfcli.2025.502264
The rapid emergence of AI within the nursing profession: adaptation to new competencies
Castillo, F. C., & Rosa-Castillo, A. (2025) · Enfermería Clínica (English Edition) · doi:10.1016/j.enfcle.2025.502264
Pennsylvania may be short 20,000 nurses by 2026
Montgomery, K. A., & Glasgow, M. E. S. (2025) · doi:10.64628/aai.nk37n3khu
Takefuji, Y. (2025) · International Journal of Healthcare Management · doi:10.1080/20479700.2025.2546530
Chiaranai, C. (2025) · Pacific Rim International Journal of Nursing Research · doi:10.60099/prijnr.2025.275177
AI in Psychiatric and Mental Health Nursing: Research Expansion and Peer-Review Policy at JKPMHN
Jung, M. (2025) · Journal of Korean Academy of Psychiatric and Mental Health Nursing · doi:10.12934/jkpmhn.2025.34.3.243
Upendra, S., Kaur, J., & Kumar, R. (2025) · Springer eBooks · doi:10.1007/978-3-031-94302-7_32
Meza-Palacios, R., Lasserre, A. A. A., Márquez, A. E. B., & Carrillo, G. D. (2025) · Tendencias en energías renovables y sustentabilidad . · doi:10.56845/terys.v4i1.513
Understanding Nursing Students’ Perceptions of Artificial Intelligence and Its Future in Healthcare
Amponsah, B., Frimpong, M., & Adomako, E. (2022) · Journal of integrative nursing and palliative care · doi:10.51847/dzlsfexxgi
Heryyanoor, H., Margono, Nugroho, S. A., & Pandin, M. G. R. (2025) · Research Square · doi:10.21203/rs.3.rs-8092188/v1
Gandhi, G. C., Chakraborty, R., Kewlani, N., Saini, D. K., & Zia, K. (2025) · Springer eBooks · doi:10.1007/978-981-96-9346-7_18
Aligning humans and digital systems: Holistic workforce management in healthcare’s adaptive era
Imam, H. (2025) · Journal of Healthcare Administration · doi:10.33546/joha.3403
Technology in pandemic period and future nursing practices: a literature review
Yüksekol, A., & Serin, E. K. (2025) · Hospital Topics · doi:10.1080/00185868.2025.2604713
Kagan, O., Schoenbaum, A. E., McGrow, K., & Batara, D. (2026) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000001467
Ethical Considerations in AI Integration
McLeod‐Sordjan, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-12402-9_3
Pu, L., D'Huyvetter, P., Algera, E., Lusi, B., Tabone, W., Helder, O. K., Ista, E., Abbink, D. A., Wallenburg, I., Wehrens, R., et al. (2026) · doi:10.2196/preprints.91488
Aus- und Weiterbildung geregelter Gesundheitsberufe in Europa
Ewers, M., & Lehmann, Y. (2026) · Springer Reference Pflege, Therapie, Gesundheit · doi:10.1007/978-3-662-61428-0_43-2
Co‐designing a Large Language Model Benchmarking Dataset for Primary Care with Nurses in Kenya
Musau, W., Obong’o, C. O., Wanjiru, S., Otiangala, D., Emmanuel-Fabula, M., & Mateen, B. A. (2025) · Ethnographic Praxis in Industry Conference Proceedings · doi:10.1111/epic.70016
Emotional Hacking and Artificial Intelligence Tools
Shaikh, S., & Shah, A. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4307-5.ch006
Nantschev, R., & Ammenwerth, E. (2026) · doi:10.2196/preprints.95546
From Big Data to Smart Health: Machine Learning Approaches Shaping the Future of Biomedicine
Manoharan, J., Srinivasan, V., & Plunkett, C. M. (2026) · Journal of Medico Informatics · doi:10.64659/jomi/215258
AI-Driven Security in Medical Robotics for Safe Patient Care
Channi, H. K. (2026) · Studies in big data · doi:10.1007/978-3-032-18871-7_11
Mohamed, N. A., Ali, S. O., Ramadan, O. M. E., Farghaly, S. M., Elkhider, E. E., & Zaky, M. E. (2026) · BMC Nursing · doi:10.1186/s12912-026-04706-9
Importance of AI Chatbot in Nursing Education: A Systematic Review
Ghaffar, T., Balakrishnan, R., & Javaid, M. A. A. (2026) · Biological and Clinical Sciences Research Journal · doi:10.54112/bcsrj.v7i4.2271
Alhumaidi, B. N., & Alharbi, T. A. F. (2026) · Healthcare · doi:10.3390/healthcare14101407 · Cited by 2 · see citing works
Adaptive Computational Intelligence for Next-Generation Nursing and Assistive Robotics
Cárdenas, M.-I., Avgousti, S., Christoforou, E. G., & Vourkos, E. G. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536590
Meena, A. K., Sahu, V. K., Shakyawal, A., & Chaturvedi, T. (2026) · International Journal of Innovations in Science Engineering and Management. · doi:10.69968/ijisem.2026v5i2340-347 · Cited by 1 · see citing works
Ukaigwe, P. U. (2026) · Creative Education · doi:10.4236/ce.2026.176060
Plotting the Course for Artificial Intelligence in Nursing Education Research Methodologies (Part 1)
Hollinger-Smith, L. M. (2026) · Nursing Education Perspectives · doi:10.1097/01.nep.0000000000001557
Mazrouei, N. A., Alhosani, A., Almutairi, A., Alghunaiman, A., Ibrahim, R. M., Ali, W. K. A., Al-Kubaissi, K. A., Ibrahim, O. M., Abdel‐Qader, D. H., Saeed, A. A., et al. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01680-8
- 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 · 2022: 1
- 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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