Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare
Open accessAbstract
This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The absence of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses to engage effectively with these technologies.
“The absence of standardized AI literacy programs and insufficient integration of AI concepts into nursing curricula limit the preparedness of future nurses to engage effectively with these technologies [39,47].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [39]
The Review section says nursing curricula must incorporate AI coursework to prepare future nurses to use AI effectively. It does not establish that standardized AI literacy programs are absent or document the current extent of AI integration in nursing curricula. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe 18 included studies collectively explored diverse applications of AI in nursing across research, practice, and education.
“The 18 included studies in this umbrella review collectively explored the diverse applications of AI in nursing across research, practice, and education [32-49].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [39]
This disconnect hinders the realization of AI's transformative potential in real-world nursing environments.
“This disconnect hinders the realization of AI’s transformative potential in real-world nursing environments, where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [39]
Systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed.
“where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [39]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
An overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · 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
Big data in healthcare: management, analysis and future prospects
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0217-0 · Cited by 1,747 · see citing works
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Teamwork in healthcare: Key discoveries enabling safer, high-quality care.
Rosen, M. A., DiazGranados, D., Dietz, A. S., Benishek, L. E., Thompson, D., Pronovost, P. J., & Weaver, S. J. (2018) · American Psychologist · doi:10.1037/amp0000298 · Cited by 1,283 · see citing works
The role of artificial intelligence in healthcare: a structured literature review
Secinaro, S. F., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01488-9 · Cited by 1,203 · see citing works
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works
Privacy and artificial intelligence: challenges for protecting health information in a new era
Murdoch, B. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00687-3 · Cited by 991 · see citing works
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928 · see citing works
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
Addressing bias in big data and AI for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903 · see citing works
Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103655 · Cited by 811 · see citing works
The impact of artificial intelligence in medicine on the future role of the physician
Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789 · see citing works
Ethics and governance of trustworthy medical artificial intelligence
Zhang, J., & Zhang, Z.-M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02103-9 · Cited by 507 · cited 1× · see citing works
“These structures also play a crucial role in ensuring that AI systems align with the organization's mission and values while focusing on patient safety, data security, and ethical patient care [57].”
BackgroundArtificial intelligence in clinical and genomic diagnostics
Dias, R., & Torkamani, A. (2019) · Genome Medicine · doi:10.1186/s13073-019-0689-8 · Cited by 474 · see citing works
Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00063-2 · Cited by 461 · see citing works
Ethical Issues of Artificial Intelligence in Medicine and Healthcare
Farhud, D. D., & Zokaei, S. (2021) · Iranian Journal of Public Health · doi:10.18502/ijph.v50i11.7600 · Cited by 416 · see citing works
Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021) · Nature Communications · doi:10.1038/s41467-021-20910-4 · Cited by 415 · see citing works
SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358 · see citing works
Artificial intelligence in health care: accountability and safety
Habli, I., Lawton, T., & Porter, Z. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237487 · Cited by 357 · 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 336 · see citing works
AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319 · see citing works
Artificial intelligence education programs for health care professionals: Scoping review
Charow, R., Jeyakumar, T., Younus, S., Dolatabadi, E., Salhia, M., Al-Mouaswas, D., Anderson, M., Balakumar, S., Clare, M., Dhalla, A., et al. (2021) · JMIR Medical Education · doi:10.2196/31043 · Cited by 291 · see citing works
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281 · see citing works
A Review on Innovation in Healthcare Sector (Telehealth) through Artificial Intelligence
Amjad, A., Kordel, P., & Fernandes, G. (2023) · Sustainability · doi:10.3390/su15086655 · Cited by 245 · see citing works
Quantum computing for healthcare: a review
Ur Rasool, R., Ahmad, H. F., Rafique, W., Qayyum, A., Qadir, J., & Anwar, Z. (2023) · Future Internet · doi:10.3390/fi15030094 · Cited by 242 · see citing works
How can we manage biases in artificial intelligence systems – A systematic literature review
P．S．, D. V. (2023) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2023.100165 · Cited by 239 · see citing works
Landers, R. N., & Behrend, T. S. (2023) · American Psychologist · doi:10.1037/amp0000972 · Cited by 233 · see citing works
Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105917 · Cited by 221 · see citing works
The role of artificial intelligence in medical imaging research
Tang, X. (2019) · BJR|Open · doi:10.1259/bjro.20190031 · Cited by 219 · see citing works
Zhou, N., Zhang, C., Lv, H., Hao, C.-X., Li, T., Zhu, J., Zhu, H., Jiang, M., Liu, K.-W., Hou, H., et al. (2018) · The Oncologist · doi:10.1634/theoncologist.2018-0255 · Cited by 212 · see citing works
Human-Centered Design to Address Biases in Artificial Intelligence
Chen, Y., Clayton, E. W., Novak, L. L., Anders, S. H., & Malin, B. (2023) · Journal of Medical Internet Research · doi:10.2196/43251 · Cited by 211 · see citing works
Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Langlotz, C. P. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.060 · Cited by 198 · 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
The History of Robotics in Surgical Specialties
Shah, J., Vyas, A. K., & Vyas, D. (2014) · American Journal of Robotic Surgery · doi:10.1166/ajrs.2014.1006 · Cited by 191 · see citing works
Zeng, D., Cao, Z., & Neill, D. B. (2021) · Artificial Intelligence in Medicine · doi:10.1016/b978-0-12-821259-2.00022-3 · Cited by 183 · see citing works
Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments
Wang, W., & Hsu, W.-S. (2023) · Sensors · doi:10.3390/s23135913 · Cited by 163 · see citing works
AI applications in functional genomics
Caudai, C., Galizia, A., Geraci, F., Pera, L. L., Morea, V., Salerno, E., Via, A., & Colombo, T. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.009 · Cited by 150 · see citing works
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance
Boukherouaa, E. B., Shabsigh, G., AlAjmi, K., et al. (2021) · Departmental Paper · doi:10.5089/9781589063952.087 · Cited by 149 · see citing works
Application of Artificial Intelligence in Pathology: Trends and Challenges
Kim, I., Kang, K., Song, Y., & Kim, T. J. (2022) · Diagnostics · doi:10.3390/diagnostics12112794 · Cited by 126 · see citing works
Remote patient monitoring systems: Applications, architecture, and challenges
Boikanyo, K., Zungeru, A. M., Sigweni, B., Yahya, A., & Lebekwe, C. K. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01638 · Cited by 123 · see citing works
Morley, J., Murphy, L., Mishra, A., Joshi, I., & Karpathakis, K. (2021) · JMIR Formative Research · doi:10.2196/31623 · Cited by 119 · see citing works
Health information technology to improve care for people with multiple chronic conditions
Samal, L., Fu, H. N. C., Camara, D. S., Wang, J., Bierman, A. S., & Dorr, D. A. (2021) · Health Services Research · doi:10.1111/1475-6773.13860 · Cited by 115 · see citing works
Bernal, J., & Mazo, C. (2022) · Applied Sciences · doi:10.3390/app122010228 · Cited by 70 · see citing works
Artificial intelligence in critical illness and its impact on patient care: a comprehensive review
Saqib, M., Iftikhar, M., Neha, F., Karishma, F., & Mumtaz, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1176192 · Cited by 66 · see citing works
Grzybowski, A., & Brona, P. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10112352 · Cited by 64 · see citing works
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 · see citing works
Eggerth, A., Hayn, D., & Schreier, G. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14045 · Cited by 54 · see citing works
Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in FinancePreprint
Boukherouaa, E. B., Shabsigh, G., AlAjmi, K., et al. (2021) · RePEc: Research Papers in Economics · doi:10.5089/9781589063952.087.a001 · Cited by 46 · see citing works
Artificial intelligence for falls management in older adult care: A scoping review of nurses' role
O’Connor, S., Gasteiger, N., Stanmore, E., Wong, D., & Lee, J. J. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13853 · Cited by 45 · see citing works
Artificial Intelligence in Surgical Learning
Pakkasjärvi, N. A., Luthra, T., & Anand, S. (2023) · Surgeries · doi:10.3390/surgeries4010010 · Cited by 45 · see citing works
Human Factors in Electronic Health Records Cybersecurity Breach: An Exploratory Analysis.
Yeo, L. H., & Banfield, J. (2022) · PubMed Central · Cited by 43 · see citing works
Navigating the AI Revolution: The Case for Precise Regulation in Health Care
Reddy, S. (2023) · Journal of Medical Internet Research · doi:10.2196/49989 · Cited by 42 · see citing works
Doctor-patient interactions in the age of AI: navigating innovation and expertise
Hryciw, B. N., Fortin, Z., Ghossein, J., & Kyeremanteng, K. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1241508 · Cited by 41 · see citing works
A smarter perspective: Learning with and from AI-cases
Arbelaez Ossa, L., Rost, M., Lorenzini, G., Shaw, D. M., & Elger, B. S. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102458 · Cited by 39 · see citing works
Technological Progress, Artificial Intelligence, and Inclusive GrowthPreprint
Schindler, M., Korinek, A., & Stiglitz, J. E. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.4026363 · Cited by 29 · see citing works
Artificial intelligence and medical research databases: ethical review by data access committees
McKay, F., Williams, B. J., Prestwich, G., Bansal, D., Treanor, D. E., & Hallowell, N. (2023) · BMC Medical Ethics · doi:10.1186/s12910-023-00927-8 · Cited by 25 · see citing works
Technological Progress, Artificial Intelligence, and Inclusive Growth
Korinek, A., Schindler, M., & Stiglitz, J. E. (2021) · RePEc: Research Papers in Economics · doi:10.5089/9781513583280.001.a001 · Cited by 19 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [66]Patient Safety: Achieving a New Standard for Care National Academies Press Institute of Medicine (US) Committee on Data Standards for Patient Safety Washington, DC National Academies Press 2004 https://pubmed.ncbi.nlm.nih.gov/25009854/
Cited by
Sorted by citations
The Role of AI in Nursing Education and Practice: Umbrella Review
El Arab, R. A., Al Moosa, O. A., Abuadas, F. H., & Somerville, J. (2025) · Journal of Medical Internet Research · doi:10.2196/69881 · Cited by 160 · see citing works
“This disconnect hinders the realization of AI’s transformative potential in real-world nursing environments, where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].”
Artificial intelligence in nursing: an integrative review of clinical and operational impacts
Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134 · see citing works
Shen, M., Shen, Y., Liu, F., & Jin, J. (2025) · BMC Nursing · doi:10.1186/s12912-025-03115-8 · Cited by 76 · see citing works
Bahari, G., Alenazi, L., & Alkathiri, A. M. (2025) · Nurse Education Today · doi:10.1016/j.nedt.2025.106816 · Cited by 10 · see citing works
- Authors
- Yelne, S., Chaudhary, M., Dod, K., Sayyad, A., & Sharma, R.
- Affiliations
- Datta Meghe Institute of Higher Education and Research (IN)
- Corresponding author
- Yelne, Seema
- Venue
- Cureus
- Published
- 2023-11-22
- Volume
- 15
- Issue
- 11
- Pages
- e49252-e49252
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC10744168
- OpenAlex
- W4388895516
- Cited by
- 190
- References
- 61
- Citations by year
- 2026: 34 · 2025: 98 · 2024: 57 · 2023: 1
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, nursing science, individualized care, diagnostic accuracy, predictive analytics, telemedicine, data privacy, algorithmic bias, AI ethics, patient-centered care, interdisciplinary collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
