Artificial intelligence in cardiac nursing practice: A systematic review of applications, challenges, and patient outcomes
DOI:
https://doi.org/10.35335/midwifery.v13i6.2250Keywords:
Artificial Intelligence, Cardiac Nursing, Machine Learning, Patient OutcomesAbstract
Cardiovascular diseases remain the leading cause of global mortality, requiring innovative approaches to improve cardiac care. This systematic review aimed to examine the applications of Artificial Intelligence (AI) in cardiac nursing practice, identify implementation challenges, and assess its impact on patient outcomes. The review was conducted in accordance with PRISMA 2020 guidelines, using PubMed, ScienceDirect, CINAHL, and Web of Science. Original studies published between 2020 and 2025 that explicitly addressed nursing roles in cardiac care were included. Eleven studies met the inclusion criteria and were appraised using the Joanna Briggs Institute (JBI) tools. The results show that AI applications, including ChatGPT and machine learning models, support cardiac nursing through clinical decision support, patient education, risk prediction, and home-based monitoring. These applications were associated with improved nursing efficiency, enhanced patient self-management, early detection of clinical deterioration, and potential reduction in hospitalization. However, challenges such as data accuracy, ethical concerns, algorithm transparency, and limited digital literacy among nurses were consistently reported. In conclusion, AI has strong potential to enhance evidence-based and patient-centered cardiac nursing practice. Successful integration requires ethical governance, adequate training, and interdisciplinary collaboration to ensure safe and effective implementation.
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Al-Dhafeeri, B. N. N., Alanizi, M. K. M., Al-Anazi, M. F. A., Alanezi, S. S., Aldafeery, B. R., Beshi, S. A. Al, Alsulami, A. O., & Sharyah, M. S. N. AL. (2024). The Role of Nursing in the Management of Cardiac Disease and Improving Health Care Outcomes for Cardiac Disease Patients. Journal of International Crisis and Risk Communication Research, 7, 1–9. https://search.proquest.com/openview/2c61fd6e0fc561aa51504890507e6d8f/1?pq-origsite=gscholar&cbl=6480378
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., Lett, E., Menon, B. K., Sheehan, K. A., & Al-Zaiti, S. S. (2017). Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. In Physiology & behavior (Vol. 176, Issue 3). https://doi.org/10.1161/CIR.0000000000001201.Use
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095
Ball Dunlap, P. A., & Michalowski, M. (2024). Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education. JMIR Nursing, 7. https://doi.org/10.2196/62678
Chae, S., Davoudi, A., Song, J., Evans, L., Bowles, K. H., Mcdonald, M. V., Barrón, Y., Min, S. H., Oh, S., Scharp, D., Xu, Z., & Topaz, M. (2025). Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure. Journal of Nursing Scholarship, 57(1), 165–177. https://doi.org/10.1111/jnu.13030
Dailah, H. G., Koriri, M., Sabei, A., Kriry, T., & Zakri, M. (2024). Artificial Intelligence in Nursing: Technological Benefits to Nurse’s Mental Health and Patient Care Quality. Healthcare (Switzerland), 12(24). https://doi.org/10.3390/healthcare12242555
Di Cesare, M., McGhie, D. V., Perel, P., Mwangi, J., Taylor, S., Pervan, B., Kabudula, C., Narula, J., Bixby, H., Pineiro, D., Gaziano, T. A., & Pinto, F. J. (2024). The Heart of the World. Global Heart, 19(1). https://doi.org/10.5334/gh.1288
Gala, D., Behl, H., Shah, M., & Makaryus, A. N. (2024). The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Switzerland), 12(4). https://doi.org/10.3390/healthcare12040481
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023). Artificial Intelligence in Nursing Education: Opportunities and Challenges. Hawaii Journal of Health and Social Welfare, 82(12), 302–305.
Harskamp, R. E., & De Clercq, L. (2024). Performance of ChatGPT as an AI-assisted decision support tool in medicine: a proof-of-concept study for interpreting symptoms and management of common cardiac conditions (AMSTELHEART-2). Acta Cardiologica, 79(3), 358–366. https://doi.org/10.1080/00015385.2024.2303528
Hillmann, H. A. K., Angelini, E., Karfoul, N., Feickert, S., Mueller-Leisse, J., & Duncker, D. (2024). Accuracy and comprehensibility of chat-based artificial intelligence for patient information on atrial fibrillation and cardiac implantable electronic devices. Europace, 26(1), 1–10. https://doi.org/10.1093/europace/euad369
Jenča, D., Melenovský, V., Stehlik, J., Staněk, V., Kettner, J., Kautzner, J., Adámková, V., & Wohlfahrt, P. (2021). Heart failure after myocardial infarction: incidence and predictors. ESC Heart Failure, 8(1), 222–237. https://doi.org/10.1002/ehf2.13144
King, R. C., Samaan, J. S., Yeo, Y. H., Mody, B., Lombardo, D. M., & Ghashghaei, R. (2024). Appropriateness of ChatGPT in Answering Heart Failure Related Questions. Heart Lung and Circulation, 33(9), 1314–1318. https://doi.org/10.1016/j.hlc.2024.03.005
Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., David Albert Newton, J., Gallagher, T., Steinert, S., Filtness, A. J., & Reniers, G. (2023). The risks of using ChatGPT to obtain common safety-related information and advice. Safety Science, 167(June), 106244. https://doi.org/10.1016/j.ssci.2023.106244
Pan, M., & Zhang, L. (2024). Effect of predictive nursing based on risk early warning system on patients with acute respiratory failure in Intensive Care Unit. Pakistan Journal of Medical Sciences, 40(8), 1819–1824. https://doi.org/10.12669/pjms.40.8.9506
Pham, C., Govender, R., Tehami, S., Chavez, S., Adepoju, O. E., & Liaw, W. (2024). ChatGPT’s Performance in Cardiac Arrest and Bradycardia Simulations Using the American Heart Association’s Advanced Cardiovascular Life Support Guidelines: Exploratory Study. Journal of Medical Internet Research, 26(1). https://doi.org/10.2196/55037
Ramadan, O. M. E., Alruwaili, M. M., Alruwaili, A. N., Elsehrawy, M. G., & Alanazi, S. (2024). Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses’ perspectives. BMC Nursing, 23(1). https://doi.org/10.1186/s12912-024-02571-y
Rouhi, A. D., Ghanem, Y. K., Yolchieva, L., Saleh, Z., Joshi, H., Moccia, M. C., Suarez-Pierre, A., & Han, J. J. (2024). Can Artificial Intelligence Improve the Readability of Patient Education Materials on Aortic Stenosis? A Pilot Study. Cardiology and Therapy, 13(1), 137–147. https://doi.org/10.1007/s40119-023-00347-0
Scquizzato, T., Semeraro, F., Swindell, P., Simpson, R., Angelini, M., Gazzato, A., Sajjad, U., Bignami, E. G., Landoni, G., Keeble, T. R., & Mion, M. (2024). Testing ChatGPT ability to answer laypeople questions about cardiac arrest and cardiopulmonary resuscitation. Resuscitation, 194(November 2023), 110077. https://doi.org/10.1016/j.resuscitation.2023.110077
Van Bulck, L., & Moons, P. (2024). What if your patient switches from Dr. Google to Dr. ChatGPT? A vignette-based survey of the trustworthiness, value, and danger of ChatGPT-generated responses to health questions. European Journal of Cardiovascular Nursing, 23(1), 95–98. https://doi.org/10.1093/eurjcn/zvad038
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025). The integration of AI in nursing: addressing current applications, challenges, and future directions. Frontiers in Medicine, 12(4). https://doi.org/10.3389/fmed.2025.1545420
Xu, Y., Liu, Q., Pang, J., Zeng, C., Ma, X., Li, P., Ma, L., Huang, J., & Xie, H. (2024). Assessment of Personalized Exercise Prescriptions Issued by ChatGPT 4.0 and Intelligent Health Promotion Systems for Patients with Hypertension Comorbidities Based on the Transtheoretical Model: A Comparative Analysis. Journal of Multidisciplinary Healthcare, 17(October), 5063–5078. https://doi.org/10.2147/JMDH.S477452
Yip, S. S. W., Ning, S., Wong, N. Y. K., Chan, J., Ng, K. S., Kwok, B. O. T., Anders, R. L., & Lam, S. C. (2025). Leveraging machine learning in nursing: innovations, challenges, and ethical insights. Frontiers in Digital Health, 7(May), 1–13. https://doi.org/10.3389/fdgth.2025.1514133
Yiryuo, L., Kpekura, S., Osman, W., Kukeba, M. W., Mumuni, N. D., Mwinbam, M. M., & Dery, A. (2024). Challenges and support experienced by family caregivers seeking antiretroviral therapy services for children living with HIV/AIDS: a phenomenological study in Ghana. BMJ Open, 14(5). https://doi.org/10.1136/bmjopen-2023-081036
