SYSTEMATIC REVIEW
Ethical and regulatory considerations in the use of AI and machine learning in nursing: A systematic review
Sobhia Ahmed Abdel Qader Mohammed ,
Yasmine Mahmoud Moussa Osman PhD,
Ateya Megahed Ibrahim PhD,
Mostafa Shaban PhD,
Sobhia Ahmed Abdel Qader Mohammed
Assistant Professor
King Khalid University, Abha, Kingdom of Saudi Arabia
Search for more papers by this authorYasmine Mahmoud Moussa Osman PhD
Lecturer
Hiroshima University, Hiroshima, Japan
Search for more papers by this authorAteya Megahed Ibrahim PhD
Assistant professor
Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Search for more papers by this authorCorresponding Author
Mostafa Shaban PhD
Lecturer
Jouf University, Sakaka, Saudi Arabia
Correspondence
Mostafa Shaban, Community Health Nursing Department, College of Nursing, Jouf University, Sakaka, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorSobhia Ahmed Abdel Qader Mohammed ,
Yasmine Mahmoud Moussa Osman PhD,
Ateya Megahed Ibrahim PhD,
Mostafa Shaban PhD,
Sobhia Ahmed Abdel Qader Mohammed
Assistant Professor
King Khalid University, Abha, Kingdom of Saudi Arabia
Search for more papers by this authorYasmine Mahmoud Moussa Osman PhD
Lecturer
Hiroshima University, Hiroshima, Japan
Search for more papers by this authorAteya Megahed Ibrahim PhD
Assistant professor
Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Search for more papers by this authorCorresponding Author
Mostafa Shaban PhD
Lecturer
Jouf University, Sakaka, Saudi Arabia
Correspondence
Mostafa Shaban, Community Health Nursing Department, College of Nursing, Jouf University, Sakaka, Saudi Arabia.
Email: [email protected]
Search for more papers by this authorAbstract
Aim
This study systematically explores the ethical and regulatory considerations surrounding the integration of artificial intelligence (AI) and machine learning (ML) in nursing practice, with a focus on patient autonomy, data privacy, algorithmic bias, and accountability.
Background
AI and ML are transforming nursing practice by enhancing clinical decision-making and operational efficiency. However, these technologies present significant ethical challenges related to ensuring patient autonomy, safeguarding data privacy, mitigating algorithmic bias, and ensuring transparency in decision-making processes. Current frameworks are not sufficiently tailored to nursing-specific contexts.
Methods
A systematic review was conducted, adhering to PRISMA guidelines. Six major databases were searched for studies published between 2000 and 2024. Seventeen studies met the inclusion criteria and were included in the final analysis.
Results
Five key themes emerged from the review: enhancement of clinical decision-making, promotion of ethical awareness, support for routine nursing tasks, challenges in algorithmic bias, and the importance of public engagement in regulatory frameworks. The review identified critical gaps in nursing-specific ethical guidelines and regulatory oversight for AI integration in practice.
Discussion
AI technologies offer substantial benefits for nursing, particularly in decision-making and task efficiency. However, these advantages must be balanced against ethical concerns, including the protection of patient rights, algorithmic transparency, and bias mitigation. Current regulatory frameworks require adaptation to meet the ethical needs of nursing.
Conclusion and implications for nursing and health policy
The findings emphasize the need for the development of nursing-specific ethical guidelines and robust regulatory frameworks to ensure the responsible integration of AI technologies into nursing practice. AI integration must uphold ethical principles while enhancing the quality of care.
CONFLICT OF INTEREST STATEMENT
All authors declare no conflicts of interest.
REFERENCES
- Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J. & Gao, G. (2023) Addressing algorithmic bias and the perpetuation of health inequities: an AI bias aware framework. Health Policy and Technology, 12(1), 100702. https://doi.org/10.1016/j.hlpt.2022.100702
- Ahmed, M.I., Spooner, B., Isherwood, J., Lane, M., Orrock, E. & Dennison, A. (2023) A systematic review of the barriers to the implementation of artificial intelligence in healthcare. Cureus, 15(10), e46454. https://doi.org/10.7759/cureus.46454
- Ahmed, S.K. (2024) Artificial intelligence in nursing: current trends, possibilities and pitfalls. Journal of Medicine, Surgery, and Public Health, 3, 100072. https://doi.org/10.1016/j.glmedi.2024.100072
10.1016/j.glmedi.2024.100072 Google Scholar
- Akingbola, A., Adeleke, O., Idris, A., Adewole, O. & Adegbesan, A. (2024) Artificial intelligence and the dehumanization of patient care. Journal of Medicine, Surgery, and Public Health, 3, 100138. https://doi.org/10.1016/j.glmedi.2024.100138
10.1016/j.glmedi.2024.100138 Google Scholar
- 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) A review of the role of artificial intelligence in healthcare. Journal of Personalized Medicine, 13(6), 951. https://doi.org/10.3390/jpm13060951
- Alanzi, T., Alhajri, A., Almulhim, S., Alharbi, S., Alfaifi, S., Almarhoun, E., Mulla, R., Alasafra, Z.O., Alalwan, Z., Alnasser, F., Almukhtar, F., Al Ghadeer, F., Amro, S., Alodhayb, I. & Alanzi, N. (2023) Artificial intelligence and patient autonomy in obesity treatment decisions: an empirical study of the challenges. Cureus, 15(11), e49725. https://doi.org/10.7759/cureus.49725
- Aldoseri, A., Al-Khalifa, K.N. & Hamouda, A.M. (2023) Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. https://doi.org/10.3390/app13127082
- Alotaibi, Y.K. & Federico, F. (2017) The impact of health information technology on patient safety. Saudi Medical Journal, 38(12), 1173–1180. https://doi.org/10.15537/smj.2017.12.20631
- 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., Al Yami, M. S., Al Harbi, S. & Albekairy, A. M. (2023) Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z
- Amann, J., Blasimme, A., Vayena, E., Frey, D. & Madai, V.I. (2020) Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1–9. https://doi.org/10.1186/s12911-020-01332-6
- Arbelaez Ossa, L., Lorenzini, G., Milford, S.R., Shaw, D., Elger, B.S. & Rost, M. (2024) Integrating ethics in AI development: a qualitative study. BMC Medical Ethics, 25(1), 10. https://doi.org/10.1186/s12910-023-01000-0
- Atalla, A.D.G., El-Ashry, A.M. & Mohamed Sobhi Mohamed, S. (2024) The moderating role of ethical awareness in the relationship between nurses’ artificial intelligence perceptions, attitudes, and innovative work behavior: a cross-sectional study. BMC Nursing, 23(1), 1–11. https://doi.org/10.1186/s12912-024-02143-0
- 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
- Booth, R.G., Strudwick, G., McBride, S., O'Connor, S. & Solano López, A.L. (2021) How the nursing profession should adapt for a digital future. BMJ, 373, n1190. https://doi.org/10.1136/bmj.n1190
10.1136/bmj.n1190 Google Scholar
- Brock, S. (2023) Nursing ethics in the age of artificial intelligence: ethical dilemmas and solutions, Journal of Advanced Practices in Nursing, 8, 3–4. https://doi.org/10.37421/2573-0347.2023.8.342
10.37421/2573?0347.2023.8.342 Google Scholar
- Chen, Y., Moreira, P., Liu, W., Monachino, M., Nguyen, T.L.H. & Wang, A. (2022) Is there a gap between artificial intelligence applications and priorities in health care and nursing management? Journal of Nursing Management, 30(8), 3736–3742. https://doi.org/10.1111/jonm.13851
- Cheraghi, R., Valizadeh, L., Zamanzadeh, V., Hassankhani, H. & Jafarzadeh, A. (2023) Clarification of ethical principle of the beneficence in nursing care: an integrative review. BMC Nursing, 22(1), 89. https://doi.org/10.1186/s12912-023-01246-4
- Colonna, L. & Greenstein, S. (2022) Law in the era of artificial intelligence. Available at: https://irilaw.org/wp-content/uploads/2022/02/law-in-the-era-of-artificial-intelligence.pdf
- Combi, C., Amico, B., Bellazzi, R., Holzinger, A., Moore, J.H., Zitnik, M. & Holmes, J.H. (2022) A manifesto on explainability for artificial intelligence in medicine. Artificial Intelligence in Medicine, 133, 102423. https://doi.org/10.1016/j.artmed.2022.102423
- Couture, V., Roy, M.C., Dez, E., Laperle, S. & Bélisle-Pipon, J.C. (2023) Ethical implications of artificial intelligence in population health and the public's role in its governance: perspectives from a citizen and expert panel. Journal of Medical Internet Research, 25, 1–14. https://doi.org/10.2196/44357
- FDA. (2024) Artificial intelligence and medical products: how CBER, CDER, CDRH, and OCP are working together. https://www.fda.gov/media/177030/download?attachment
- Forcier, M.B., Gallois, H., Mullan, S. & Joly, Y. (2019) Integrating artificial intelligence into health care through data access: can the GDPR act as a beacon for policymakers? Journal of Law and the Biosciences, 6(1), 317–335. https://doi.org/10.1093/jlb/lsz013
- Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R. & Elkin, P. (2024) The sociodemographic biases in machine learning algorithms: a biomedical informatics perspective. Life, 14(6), 652. https://doi.org/10.3390/life14060652
- Goh, K.H., Wang, L., Yeow, A.Y.K., Poh, H., Li, K., Yeow, J.J.L. & Tan, G.Y.H. (2021) Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature Communications, 12(1), 711. https://doi.org/10.1038/s41467-021-20910-4
- Gopal, D.P., Chetty, U., O'Donnell, P., Gajria, C. & Blackadder-Weinstein, J. (2021) Implicit bias in healthcare: clinical practice, research and decision making. Future Healthcare Journal, 8(1), 40–48. https://doi.org/10.7861/fhj.2020-0233
- Hine, C., Nilforooshan, R. & Barnaghi, P. (2022) Ethical considerations in design and implementation of home-based smart care for dementia. Nursing Ethics, 29(4), 1035–1046. https://doi.org/10.1177/09697330211062980
- Hoogendoorn, C.J. & Rodríguez, N.D. (2023) Rethinking dehumanization, empathy, and burnout in healthcare contexts. Current Opinion in Behavioral Sciences, 52, 101285. https://doi.org/10.1016/j.cobeha.2023.101285
- Ibrahim, A.M., Abdel-Aziz, H.R., Mohamed, H.A.H., Zaghamir, D.E.F., Wahba, N.M.I., Hassan, G.A., Shaban, M., EL-Nablaway, M., Aldughmi, O.N. & Aboelola, T.H. (2024) Balancing confidentiality and care coordination: challenges in patient privacy. BMC Nursing, 23(1), 564. https://doi.org/10.1186/s12912-024-02231-1
- Ibuki, T., Ibuki, A. & Nakazawa, E. (2024) Possibilities and ethical issues of entrusting nursing tasks to robots and artificial intelligence. Nursing Ethics, 31(6), 1010–1020. https://doi.org/10.1177/09697330221149094
- Karalis, V.D. (2024) The integration of artificial intelligence into clinical practice. Applied Biosciences, 3(1), 14–44. https://doi.org/10.3390/applbiosci3010002
- Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A. & Qadir, J. (2023) Privacy-preserving artificial intelligence in healthcare: techniques and applications. Computers in Biology and Medicine, 158, 106848. https://doi.org/10.1016/j.compbiomed.2023.106848
- Khosravi, M., Zare, Z., Mojtabaeian, S.M. & Izadi, R. (2024) Artificial intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Services Research and Managerial Epidemiology, 11, 23333928241234863. https://doi.org/10.1177/23333928241234863
- Maleki Varnosfaderani, S. & Forouzanfar, M. (2024) The role of AI in hospitals and clinics: transforming healthcare in the 21st century. Bioengineering, 11(4), 337. https://doi.org/10.3390/bioengineering11040337
- Martinez-Ortigosa, A., Martinez-Granados, A., Gil-Hernández, E., Rodriguez-Arrastia, M., Ropero-Padilla, C. & Roman, P. (2023) Applications of artificial intelligence in nursing care: a systematic review. Journal of Nursing Management, 2023, 1–12. https://doi.org/10.1155/2023/3219127
- Mennella, C., Maniscalco, U., De Pietro, G. & Esposito, M. (2024) Ethical and regulatory challenges of AI technologies in healthcare: a narrative review. Heliyon, 10(4), e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
- Naik, N., Hameed, B.M.Z., Shetty, D.K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., Smriti, K., Shetty, S., Rai, B.P., Chlosta, P. & Somani, B.K. (2022) Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Frontiers in Surgery, 9, 862322. https://doi.org/10.3389/fsurg.2022.862322
- Naseri, S., Shukla, S., Hiwale, K., Jagtap, M.M., Gadkari, P., Gupta, K., Deshmukh, M. & Sagar, S. (2024) From pixels to prognosis: a narrative review on artificial intelligence's pioneering role in colorectal carcinoma histopathology. Cureus, 16(4), e59171. https://doi.org/10.7759/cureus.59171
- Ng, Z.Q.P., Ling, L.Y.J., Chew, H.S.J. & Lau, Y. (2022) The role of artificial intelligence in enhancing clinical nursing care: a scoping review. Journal of Nursing Management, 30(8), 3654–3674. https://doi.org/10.1111/jonm.13425
- Norori, N., Hu, Q., Aellen, F.M., Faraci, F.D. & Tzovara, A. (2021) Addressing bias in big data and AI for health care: a call for open science. Patterns, 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347
- Obermeyer, Z. & Emanuel, E.J. (2016) Predicting the future: big data, machine learning, and clinical medicine. New England Journal of Medicine, 375, 1216–1219. https://doi.org/10.1056/NEJMp1606181
- Oduoye, M.O., Javed, B., Gupta, N. & Valentina Sih, C.M. (2023) Algorithmic bias and research integrity; the role of nonhuman authors in shaping scientific knowledge with respect to artificial intelligence: a perspective. International Journal of Surgery (London, England), 109(10), 2987–2990. https://doi.org/10.1097/JS9.0000000000000552
- Palaniappan, K., Lin, E.Y.T. & Vogel, S. (2024) Global regulatory frameworks for the use of artificial intelligence (AI) in the healthcare services sector. Healthcare, 12(5), 562. https://doi.org/10.3390/healthcare12050562
- Palaniappan, K., Lin, E.Y.T., Vogel, S. & Lim, J.C.W. (2024) Gaps in the global regulatory frameworks for the use of artificial intelligence (AI) in the healthcare services sector and key recommendations. Healthcare, 12(17), 1730. https://doi.org/10.3390/healthcare12171730
- Pearson, G.S. (2024) Artificial intelligence and publication ethics. Journal of the American Psychiatric Nurses Association, 30(3), 453–455. https://doi.org/10.1177/10783903241245423
- Pool, J., Akhlaghpour, S., Fatehi, F. & Burton-Jones, A. (2024) A systematic analysis of failures in protecting personal health data: a scoping review. International Journal of Information Management, 74, 102719. https://doi.org/10.1016/j.ijinfomgt.2023.102719
- 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
- Rony, M.K.K., Alrazeeni, D.M., Akter, F., Nesa, L., Chandra Das, D., Uddin, M.J., Begum, J., Tahmina Khatun, M., Noor, M.A., Ahmad, S., Tanha, S.M., Deb, T.R. & Rina Parvin, M. (2024) The role of artificial intelligence in enhancing nurses’ work–life balance. Journal of Medicine, Surgery, and Public Health, 3, 100135. https://doi.org/10.1016/j.glmedi.2024.100135
10.1016/j.glmedi.2024.100135 Google Scholar
- Rony, M.K.K., Numan, S.M., Akter, K., Tushar, H., Debnath, M., Johra, F.T., Akter, F., Mondal, S., Das, M., Uddin, M.J., Begum, J. & Parvin, M.R. (2024) Nurses’ perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare. Heliyon, 10(17), e36702. https://doi.org/10.1016/j.heliyon.2024.e36702
- Rony, M.K.K., Parvin, M.R. & Ferdousi, S. (2024) Advancing nursing practice with artificial intelligence: enhancing preparedness for the future. Nursing Open, 11(1). https://doi.org/10.1002/nop2.2070
- Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F. & Wolf-Ostermann, K. (2021). Application scenarios for artificial intelligence in nursing care: rapid review. Journal of Medical Internet Research, 23(11), e26522. https://doi.org/10.2196/26522
- Siala, H. & Wang, Y. (2022) SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Social Science & Medicine, 296, 114782. https://doi.org/10.1016/j.socscimed.2022.114782
- Thirsk, L.M., Panchuk, J.T., Stahlke, S. & Hagtvedt, R. (2022) Cognitive and implicit biases in nurses’ judgment and decision-making: a scoping review. International Journal of Nursing Studies, 133, 104284. https://doi.org/10.1016/j.ijnurstu.2022.104284
- Thomas, S.J., L'Azou, M., Barrett, A.D.T. & Jackson, N.A.C. (2016) Fast-track zika vaccine development: is it possible? New England Journal of Medicine, 375(13), 1212–1216. https://doi.org/10.1056/nejmp1609300
- 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
- Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., Tatsugami, F., Yanagawa, M., Hirata, K., Yamada, A., Tsuboyama, T., Kawamura, M., Fujioka, T. & Naganawa, S. (2024) Fairness of artificial intelligence in healthcare: review and recommendations. Japanese Journal of Radiology, 42(1), 3–15. https://doi.org/10.1007/s11604-023-01474-3
- Walker, R., Dillard-Wright, J. & Iradukunda, F. (2023) Algorithmic bias in artificial intelligence is a problem: and the root issue is power. Nursing Outlook, 71(5), 102023. https://doi.org/10.1016/j.outlook.2023.102023
- Wangpitipanit, S., Lininger, J. & Anderson, N. (2024) Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant's approach. BMC Nursing, 23(1), 529. https://doi.org/10.1186/s12912-024-02170-x
- Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S. & Rangarajan, K. (2023) Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatology Online Journal, 14(6), 788–792. https://doi.org/10.4103/idoj.idoj_543_23
Citing Literature
