Artificial intelligence transforming healthcare and nursing: A comprehensive bibliometric analysis

Vaishali Balpande; Priya Rewatkar; Pallavi Dhole; Indu Alwadkar; Kavita Gomase

doi:10.31893/multirev.2025267

Keywords:

artificial intelligence in healthcare

AI in nursing care

bibliometric analysis

healthcare technology trends

AI applications in nursing

digital transformation in medicine

Abstract

Artificial intelligence (AI) is revolutionizing healthcare and nursing by enhancing decision-making, streamlining processes, and improving patient outcomes. This bibliometric analysis explores the evolving landscape of AI applications in healthcare and nursing, highlighting key research trends, influential publications, and emerging technologies. The study examines the integration of AI tools, such as machine learning, natural language processing, and predictive analytics, in areas like disease diagnosis, personalized treatment, patient monitoring, and administrative efficiency. It also investigates the role of AI in nursing practice, emphasizing its potential to support clinical decision-making, optimize care delivery, and alleviate workforce challenges. By analyzing a vast corpus of scholarly publications, this study identifies pivotal themes, prominent authors, and leading institutions driving AI innovation in healthcare. Key findings reveal an exponential growth in research output, particularly in leveraging AI for chronic disease management, telemedicine, and predictive risk modeling. Ethical considerations, including data privacy, algorithmic bias, and patient safety, emerge as critical focal points, underscoring the need for robust frameworks to ensure responsible AI adoption. Furthermore, the study highlights interdisciplinary collaboration as a cornerstone for successful AI integration, bridging the gap between technological advancements and clinical practice. Despite its transformative potential, challenges such as skill gaps, resistance to change, and resource constraints remain barriers to widespread AI adoption in healthcare and nursing. This comprehensive analysis provides valuable insights for researchers, practitioners, and policymakers, offering a roadmap for leveraging AI to address current and future healthcare challenges. The findings underscore the transformative role of AI in reshaping healthcare delivery, enhancing nursing practice, and ultimately improving patient care on a global scale. By synthesizing current knowledge and identifying future directions, this study contributes to advancing the understanding of AI’s impact on healthcare and nursing, paving the way for more efficient, equitable, and patient-centered care systems.
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23. Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Balpande, V., Rewatkar, P., Dhole, P., Alwadkar, I., & Gomase, K. (2025). Artificial intelligence transforming healthcare and nursing: A comprehensive bibliometric analysis. Multidisciplinary Reviews, 8(9), 2025267. https://doi.org/10.31893/multirev.2025267

[1] Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://www.sciencedirect.com/science/article/pii/S1751157717300500

[2] Azzi, S., Gagnon, S., Ramirez, A., & Richards, G. (2020). Healthcare applications of artificial intelligence and analytics: A review and proposed framework. Applied Sciences, 10(18), 6553. https://www.mdpi.com/2076-3417/10/18/6553

[3] Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317

[4] Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://www.sciencedirect.com/science/article/pii/S2514664524010592

[5] Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://www.sciencedirect.com/science/article/pii/S0148296321003155

[6] Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056

[7] Gashu, K. D., Gelaye, K. A., Lester, R., & Tilahun, B. (2019). Combined effect of pill refilling and self-medication reminder system on patients’ adherence to tuberculosis treatment during continuation phase in Northwest Ethiopia: A study protocol for randomised controlled trial. BMJ Health & Care Informatics, 26(1), e100050. https://doi.org/10.1136/bmjhci-2019-100050

[8] He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://www.nature.com/articles/s41591-018-0307-0

[9] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101

[10] Jimma, B. L. (2023). Artificial intelligence in healthcare: A bibliometric analysis. Telematics and Informatics Reports, 9, 100041. https://www.sciencedirect.com/science/article/pii/S2772503023000014

[11] Kusters, R., Misevic, D., Berry, H., Cully, A., Le Cunff, Y., Dandoy, L., Díaz-Rodríguez, N., Ficher, M., Grizou, J., Othmani, A., Palpanas, T., Komorowski, M., Loiseau, P., Moulin Frier, C., Nanini, S., Quercia, D., Sebag, M., Soulié Fogelman, F., Taleb, S., … Wehbi, F. (2020). Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Frontiers in Big Data, 3, 577974. https://doi.org/10.3389/fdata.2020.577974

[12] Liu, J., Chui, K. T., Lee, L.-K., Wang, F. L., Cheung, S. K., & Hui, Y. K. (2024). Artificial Intelligence and Machine (Deep) Learning in Medical Education: A Bibliometric Analysis Based on VOSviewer and CiteSpace. 2024 International Symposium on Educational Technology (ISET), 80–86. https://ieeexplore.ieee.org/abstract/document/10685716/

[13] Maghsoudi, M., Shokouhyar, S., Ataei, A., Ahmadi, S., & Shokoohyar, S. (2023). Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes. Journal of Cleaner Production, 422, 138472. https://www.sciencedirect.com/science/article/pii/S0959652623026306

[14] McGonigle, D., & Mastrian, K. (2024). Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning. https://books.google.com/books?hl=en&lr=&id=swDvEAAAQBAJ&oi=fnd&pg=PP1&dq=4.%09McGonigle,+D.,+Mastrian,+K.,+%26+Hebda,+T.+(2021).+Nursing+informatics+and+the+foundation+of+knowledge.+Jones+%26+Bartlett+Learning.&ots=0A-sLaclww&sig=n2quNaWu9PNDCLoMt8_M4m68sCI

[15] McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., & Darzi, A. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://www.nature.com/articles/s41586-019-1799-6

[16] Meskó, B., Drobni, Z., Bényei, É., Gergely, B., & GyHorffy, Z. (2017). Digital health is a cultural transformation of traditional healthcare. Mhealth, 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5682364/

[17] Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19. https://www.sciencedirect.com/science/article/pii/S037722171500274X

[18] Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 26(4), 2141–2168. https://link.springer.com/article/10.1007/s11948-019-00165-5%23Tab1

[19] Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://www.sciencedirect.com/science/article/pii/S0277953620303919

[20] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science (New York, N.Y.), 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

[21] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259

[22] Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510

[23] Ronquillo, C. E., Peltonen, L., Pruinelli, L., Chu, C. H., Bakken, S., Beduschi, A., Cato, K., Hardiker, N., Junger, A., Michalowski, M., Nyrup, R., Rahimi, S., Reed, D. N., Salakoski, T., Salanterä, S., Walton, N., Weber, P., Wiegand, T., & Topaz, M. (2021). Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of Advanced Nursing, 77(9), 3707–3717. https://doi.org/10.1111/jan.14855

[24] Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

[25] van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

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References

How to cite