ABSTRACT:
Artificial intelligence (AI) is increasingly transforming nurse-led clinics (NLCs) by enhancing clinical decision-making, streamlining workflows, and expanding access to personalized, patient-centred care. This narrative review explores the current scope of AI integration in NLCs, synthesizing evidence from peer-reviewed literature and policy reports to highlight key applications, benefits, limitations, and future directions. AI technologies, including decision-support tools, virtual assistants, and predictive analytics, have shown promise in improving chronic disease management, triage, patient education, and remote monitoring, particularly in underserved and community settings. However, challenges such as data quality, algorithmic bias, ethical concerns, infrastructure limitations, and regulatory gaps continue to hinder widespread adoption. The review emphasizes the importance of nursing informatics competencies, interdisciplinary collaboration, and policy alignment to ensure safe, equitable, and sustainable implementation of AI in frontline nursing practice. Advancing AI integration in nurse-led care requires robust training, inclusive design, and real-world pilot studies to inform scalable, evidence-based models that align with nursing values and improve health outcomes.
Cite this article:
Mamta Choudhary. Artificial Intelligence in Nurse-Led Clinics: Scope, Limitations, and the Road Ahead. A and V Pub Journal of Nursing and Medical Research. 2026;5(1):43-8. doi: 10.52711/jnmr.2026.10
Cite(Electronic):
Mamta Choudhary. Artificial Intelligence in Nurse-Led Clinics: Scope, Limitations, and the Road Ahead. A and V Pub Journal of Nursing and Medical Research. 2026;5(1):43-8. doi: 10.52711/jnmr.2026.10 Available on: https://www.jnmronline.com/AbstractView.aspx?PID=2026-5-1-10
REFERENCES:
1. Laurant M, Van der Biezen M, Wijers N, Watananirun K, Kontopantelis E, van Vught AJ. Nurses as substitutes for doctors in primary care. Cochrane Database Syst Rev. 2018; 2018(7): CD001271. doi:10.1002/14651858.CD001271.pub3
2. Bibault JE, Gaffney SG, Bourque JM. Artificial intelligence to support clinical decision-making processes. Lancet Digit Health. 2021; 3(5): e266–e268. doi:10.1016/S2589-7500(21)00057-1
3. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017; 2(4): 230–243. doi:10.1136/svn-2017-000101
4. World Health Organization. Global strategic directions for nursing and Midwifery 2021–2025. Geneva: WHO; 2021. Available from: https://www.who.int/publications/i/item/9789240033863
5. Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med Inform Decis Mak. 2020; 20(1): 310. doi:10.1186/s12911-020-01332-6
6. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, Floridi L. The ethics of AI in health care: A mapping review. Soc Sci Med. 2020;260:113172. doi:10.1016/j.socscimed.2020.113172
7. Choudhary M, Kurien N. Predatory journals: A threat to evidence-based science. Indian J Health Sci Biomed Res. 2019;12:12–14.
8. Horrocks S, Anderson E, Salisbury C. Systematic review of whether nurse practitioners working in primary care can provide equivalent care to doctors. BMJ. 2002; 324(7341): 819–823.
9. Delamaire ML, Lafortune G. Nurses in advanced roles: A description and evaluation of experiences in 12 developed countries. OECD Health Working Papers, No. 54. Paris: OECD Publishing; 2010. doi:10.1787/5kmbrcfms5g7-en
10. Maier CB, Aiken LH. Task-shifting from physicians to nurses in primary care in 39 countries: A cross-country comparative study. Eur J Public Health. 2016; 26(6): 927–934.
11. Martínez-González NA, Rosemann T, Djalali S, Tandjung R. The effect of nurse-led care in the management of chronic diseases: A systematic review. BMJ Open. 2014; 4(3): e003800. doi:10.1136/bmjopen-2013-003800
12. Poghosyan L, Norful AA, Laugesen MJ. Removing barriers to advanced practice registered nurse care: Hospital leaders' perspectives. J Nurs Adm. 2017; 47(5): 265–270. doi:10.1097/NNA.0000000000000472
13. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018; 22(5): 1589–1604. doi:10.1109/JBHI.2017.2767063
14. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Dean J. A guide to deep learning in healthcare. Nat Med. 2019; 25(1): 24–29. doi:10.1038/s41591-018-0316-z
15. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019; 25(1): 44–56. doi:10.1038/s41591-018-0300-7
16. Blease C, Kharko A, Bernstein MH, Kaptchuk TJ. Artificial intelligence and the future of primary care: Exploratory qualitative study of UK general practitioners’ views. J Med Internet Res. 2020; 22(3): e12802. doi:10.2196/12802
17. Shull JG. Digital health and the state of interoperable electronic health records. NPJ Digit Med. 2019; 2: 84. doi:10.1038/s41746-019-0154-9
18. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment Health. 2017; 4(2): e19. doi:10.2196/mental.7785
19. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98. doi:10.7861/futurehosp.6-2-94
20. Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Meinert E. The effectiveness of artificial intelligence conversational agents in health care: Systematic review. J Med Internet Res. 2020; 22(10): e20346. doi:10.2196/20346
21. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019; 380(14): 1347–1358. doi:10.1056/NEJMra1814259
22. Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Meinert E. The effectiveness of artificial intelligence conversational agents in health care: Systematic review. J Med Internet Res. 2020; 22(10): e20346. doi:10.2196/20346
23. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Webster DR. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016; 316(22): 2402–2410. doi:10.1001/jama.2016.17216
24. Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, Creswell J. Tuberculosis detection from chest x-rays using artificial intelligence: A validation study in Bangladesh. BMJ Open. 2021; 11(4): e043983. doi:10.1136/bmjopen-2020-043983
25. Bickmore TW, Trinh H, Olafsson S, O’Leary T, Asadi R, Rickles NM. Patient and consumer safety risks when using conversational assistants for medical information: An observational study. J Med Internet Res. 2018; 20(9): e11510. doi:10.2196/11510
26. Obermeyer Z, Emanuel EJ. Predicting the future—Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216–1219. doi:10.1056/NEJMp1606181
27. Booth RG, Strudwick G, McBride S, O’Connor S, Solano López AL. How the nursing profession should adapt for a digital future. BMJ. 2021; 373: n1190. doi:10.1136/bmj.n1190
28. Topaz M, Pruinelli L, Sward KA, Waters TM, Wright D, Wilkie DJ. Advancing nursing informatics in the next decade: Recommendations from an international survey. Stud Health Technol Inform. 2020;270:1037–1041. doi:10.3233/SHTI200318
29. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018; 2(10): 719–731. doi:10.1038/s41551-018-0305-z
30. Cresswell K, Bates DW, Sheikh A. Ten key considerations for the successful implementation and adoption of large-scale health information technology. J Am Med Inform Assoc. 2022; 29(3): 539–544. doi:10.1093/jamia/ocab221
31. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Allwood D, Hughes G, Shaw S. Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017; 19(11): e367. doi:10.2196/jmir.8775
32. Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SB, Savoldelli M, Benhadj L. Artificial intelligence in health care: Laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Glob Health. 2020;16(1):52. doi:10.1186/s12992-020-00505-4
33. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. doi:10.1056/NEJMra1814259
34. Leslie D, Mazumder A, Esplin J, van der Meulen M. Ethics and governance of artificial intelligence for health: WHO guidance. Lancet Digit Health. 2021;3(7):e387–e389. doi:10.1016/S2589-7500(21)00163-2
35. Topaz M, Pruinelli L. Big data and nursing: Implications for the future. Stud Health Technol Inform. 2020;270:1232–1236. doi:10.3233/SHTI200376
36. Booth RG, Strudwick G, McBride S, O’Connor S, Solano López AL. How the nursing profession should adapt for a digital future. BMJ. 2021;373:n1190. doi:10.1136/bmj.n1190
37. Bini SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J Arthroplasty. 2021;36(6 Suppl):S264–S267. doi:10.1016/j.arth.2021.01.056
38. Mittelstadt BD. Principles alone cannot guarantee ethical AI. Nat Mach Intell. 2019;1(11):501–507. doi:10.1038/s42256-019-0114-4
39. International Council of Nurses. Global Nursing Leadership Institute Programme Report. Geneva: ICN; 2021. Available from: https://www.icn.ch/system/files/2022-01/GNLI%20Report%202021.pdf
40. World Health Organization. Global strategic directions for nursing and midwifery 2021–2025. Geneva: WHO; 2021. Available from: https://www.who.int/publications/i/item/9789240033863