INTRODUCTION:

Nurse-led clinics (NLCs) are healthcare delivery models where nurses, often with advanced training, play a central role in the assessment, diagnosis, treatment, and follow-up of patients within defined scopes of practice. These clinics have emerged globally as effective strategies to address healthcare workforce shortages, improve access to care, and manage chronic diseases more efficiently. NLCs emphasize holistic, patient-centred approaches and often operate in primary care, community settings, and specialty areas such as diabetes, hypertension, tuberculosis, and palliative care.

The core strengths of NLCs lie in continuity of care, health education, early detection, and disease prevention. As healthcare systems continue to face growing demands, nurse-led models are gaining recognition as vital components of integrated, multidisciplinary care frameworks.¹

The integration of AI into nurse-led clinics (NLCs) enhances clinical decision-making, streamlines workflows, and enables personalized care. Applications such as decision support tools, predictive models, and virtual assistants aid in diagnosis, early complication detection, and care planning. AI also supports chronic disease management, medication adherence, and follow-ups, while automating tasks like documentation and scheduling to boost nursing productivity. Moreover, remote monitoring capabilities align with global strategies to digitally empower nursing in community and telehealth settings.^2-4

Despite its potential, the adoption of AI in nurse-led settings presents substantial challenges. These include concerns around data quality, algorithmic transparency, digital literacy, clinical integration, and the ethical implications of automated decision. Furthermore, limited regulatory guidance and infrastructure gaps may hinder safe and equitable implementation, particularly in under-resourced settings.^5,6

This article examines the scope of AI applications in nurse-led clinics, critically evaluates existing limitations, and discusses strategies for responsible and sustainable adoption within nursing practice. By exploring the intersection of AI and advanced nursing roles, this work aims to contribute to informed implementation and policy development that aligns with nursing values and patient-centred care.

METHODOLOGY:

This narrative review synthesizes current knowledge on AI applications in nurse-led clinics (NLCs), explores existing limitations, and outlines future directions. A structured search of databases including PubMed, CINAHL, Scopus, IEEE Xplore (2012–2025) and references was conducted using keywords and MeSH terms related to AI, nursing, and primary care. Studies in English focusing on AI's role in nursing practice, implementation, ethics, usability, or outcomes were included, while acute care, non-peer-reviewed, and inaccessible abstracts were excluded.⁷ The narrative approach was chosen to integrate diverse evidence across an emerging interdisciplinary field. The initial search identified 243 records, with 32 articles meeting the inclusion criteria for full-text review. Relevant reports from the WHO and ANA were also examined to contextualize policy and practice implications. This synthesis supports evidence-based, nurse-led AI implementation in community clinical settings.

Evolution of Nurse-Led Clinics in Contemporary Healthcare:

Nurse-led clinics (NLCs) have evolved over decades in response to shifting healthcare demands, workforce shortages, and the need for accessible, patient-centred care. Originating in the mid-20th century in the UK and US, NLCs emerged to fill service gaps and leverage nursing expertise in areas like diabetes management, wound care, hypertension, and sexual health.⁸ Over time, the role of nurses in these clinics expanded beyond routine care to include diagnosis, prescribing medications, ordering investigations, and making referrals—activities traditionally performed by physicians. This shift was supported by policy changes and regulatory frameworks that recognized advanced nursing practice and the competencies of nurse practitioners (NPs) and clinical nurse specialists (CNSs).⁹

Nurse-led clinics (NLCs) have become a vital component of healthcare systems globally, enhancing access to care, patient satisfaction, and clinical outcomes, particularly for those with chronic conditions. Their growth reflects a broader shift toward collaborative, patient-centred care models that emphasize interprofessional teamwork and effective task-sharing. These clinics are typically led by advanced practice nurses—such as nurse practitioners and clinical nurse specialists—who are qualified to assess, diagnose, treat, and monitor patients within their defined scope of practice, contributing significantly to primary care and chronic disease management.^10-11

Nurse-led clinics (NLCs) enhance primary care by improving access, reducing wait times, and ensuring continuity through services such as preventive screenings, health education, and minor illness management—especially in underserved areas. Evidence shows these models improve outcomes, patient self-management, and satisfaction. Emphasizing holistic, patient-centred care, NLCs support the global shift toward collaborative healthcare and help alleviate physician workload amid workforce shortages.¹²

Understanding Artificial Intelligence in Nursing care:

Artificial Intelligence (AI) in healthcare simulates human cognitive functions to support tasks like learning, reasoning, and decision-making with minimal human input. It enhances diagnostic accuracy, streamlines administrative tasks, and improves clinical outcomes. A key subset, Machine Learning (ML), enables systems to identify patterns and improve performance, aiding in predictive modelling and personalized care. Natural Language Processing (NLP) further supports AI by extracting insights from unstructured clinical data. In nursing, AI-powered Clinical Decision Support Systems (CDSS) assist with early detection and intervention, reducing errors and improving patient outcomes, particularly in high-risk scenarios such as sepsis or chemotherapy-related complications. AI is also integral to predictive analytics, enabling healthcare providers to foresee adverse events such as hospital readmissions, deterioration in chronic conditions, or infection risks. For example, AI models have been applied in oncology nursing to predict chemotherapy-related complications, enhancing personalized care.¹³

AI plays a pivotal role in nursing by automating routine tasks such as documentation, vital sign monitoring, and medication tracking, thereby reducing errors, easing administrative burdens, and enhancing patient safety. Tools like AI chatbots and virtual assistants support remote monitoring, patient education, and chronic disease management. AI also aids in diagnostic imaging, nursing education through adaptive simulations, and personalized patient education. Rather than replacing nurses, AI strengthens their capacity to deliver efficient, patient-centred care.¹⁴

In nursing informatics, AI transforms data into actionable insights, enhancing clinical decision-making and evidence-based practice. By leveraging advanced analytics, pattern recognition, and predictive modelling, AI supports functions such as trend analysis, risk detection, and care optimization using electronic health records. Natural Language Processing (NLP) improves documentation efficiency and accuracy, while real-time surveillance systems aid in early detection of clinical deterioration. AI-driven dashboards further support quality improvement by tracking performance metrics and safety indicators. Integrating AI into nursing informatics ensures nurses remain key contributors in data-driven, technology-enabled healthcare.¹⁵

Current Applications of AI in Nurse-Led Clinics:

AI is increasingly used in nurse-led clinics to deliver proactive, patient-centered care, particularly for chronic conditions like diabetes, hypertension, and mental health disorders. In diabetes care, AI-enabled tools such as BlueStar® support self-management through real-time glucose tracking and personalized feedback, allowing nurses to monitor and intervene remotely. For hypertension, smart monitoring devices integrated with telehealth platforms generate alerts for uncontrolled blood pressure, enabling timely nurse-led interventions.^16,17

In mental health, AI-powered chatbots like Wysa and Woebot provide CBT-based support, track mood, and assist with early identification of issues. Nurses use these insights to guide care or escalate when needed. These AI applications extend the reach of nurse-led care, enhancing accessibility, continuity, and personalization beyond traditional settings.¹⁸

In nurse-led clinics, AI enhances efficiency, accuracy, and care reach, particularly in triage, diagnosis, patient education, and remote monitoring. AI-powered triage systems help nurses prioritize cases based on urgency, streamlining workflows—especially in primary and community care. Diagnostic tools analyse patient data to support clinical decision-making, improving accuracy in resource-limited settings.¹⁹

AI also advances patient education through chatbots and virtual assistants that provide personalized guidance, reminders, and health information, supporting chronic disease self-management. Remote monitoring technologies integrated with wearables enable continuous tracking of key health metrics, triggering alerts for early intervention. Collectively, AI empowers nurse-led clinics to deliver timely, data-driven, and patient-centred care.²⁰

Benefits and Opportunities of AI in NLCs:

a) Improved decision-making and workflow efficiency:

In nurse-led and multidisciplinary settings, AI enhances clinical decision-making by translating complex data into actionable insights. Leveraging machine learning and predictive analytics, it enables early detection of complications and supports evidence-based, proactive care. AI also streamlines workflows through automation of documentation, triage, scheduling, and medication reconciliation. Tools such as natural language processing and chatbots reduce administrative burden, improving productivity, minimizing burnout, and optimizing resource utilization for high-quality, efficient care delivery. ²¹

b) Enhanced patient engagement and outcomes:

AI enhances patient-centred care by enabling personalized, bidirectional communication and adaptive support, particularly in nurse-led and community settings. Virtual assistants and wearable-integrated systems foster engagement, timely interventions, and continuity of care. By offering multilingual, accessible information, AI also promotes health equity. These tools improve adherence, symptom monitoring, and preventive behaviours, while predictive analytics support proactive, coordinated care for improved long-term outcomes across diverse populations.²²

c) Reducing burden on nurses and improving access to care:

AI is transforming nursing by automating routine tasks, reducing workload, and expanding access to care, particularly in resource-limited settings. In nurse-led clinics, tools like triage chatbots and scheduling algorithms streamline workflows, while virtual care models enable remote monitoring for underserved populations. AI also supports task-sharing by guiding nurses through evidence-based protocols, helping address workforce shortages and enhance system efficiency and responsiveness.^21,22

d) Role in underserved and rural settings.:

AI offers strategic solutions to reduce healthcare disparities in underserved and rural areas by enhancing local care delivery and accessibility. Mobile diagnostics and AI-enabled imaging empower nurses and community health workers to detect conditions like diabetic retinopathy and tuberculosis at the point of care, with remote specialist review. AI supports task-shifting through guided algorithms and improves health literacy via virtual assistants and multilingual chatbots. Integrating AI into community programs promotes early intervention, prevents complications, and advances equitable, sustainable care.^23-25

Limitations and Ethical Considerations in Using AI in NLCs ^26-29

a) Data privacy and Patient Consent:

AI in healthcare raises ethical concerns, particularly regarding data privacy and informed consent. The use of large volumes of sensitive health data heightens risks of breaches and unauthorized use, often without patients’ clear understanding, especially in low-literacy contexts. Even anonymized data may be re-identified, posing additional risks. These challenges underscore the need for robust governance frameworks that ensure transparent, purpose-limited data use and meaningful, informed consent to maintain public trust in AI-driven care.

b) Bias and Algorithmic Inequality:

A key ethical concern in healthcare AI is algorithmic bias, which can exacerbate health disparities. Models trained on unrepresentative data may yield inaccurate outcomes for marginalized groups, leading to delayed care or resource misallocation. The opacity of “black box” algorithms further limits transparency and accountability, especially in community and nurse-led settings. Ensuring equitable care requires diverse datasets, routine bias auditing, and inclusive, interdisciplinary input in AI development.

c) Nurse Readiness, Digital Literacy, and Training Needs:

The integration of AI in clinical practice is challenged by disparities in nurse readiness, digital literacy, and access to structured training. Limited exposure in nursing curricula and continuing education undermines confidence in using AI tools, risking misuse or underutilization and affecting clinical judgment. These challenges are intensified in resource-limited settings with inadequate digital infrastructure. Equitable adoption requires investment in AI-focused education, ethical training, and interdisciplinary collaboration to empower nurses to engage effectively with emerging technologies.

d) Legal, professional, and accountability concerns:

The integration of AI in nursing care presents legal, professional, and accountability challenges, particularly when clinical decisions rely on opaque algorithms. Uncertainty over liability in adverse outcomes, whether with clinicians, institutions, or AI developers, complicates professional accountability, especially for nurses. The lack of standardized guidelines further increases medicolegal risks. Clear governance frameworks, defined roles, and safeguards are essential to ensure AI supports clinical expertise while upholding patient safety and professional responsibility.

Barriers to Implementation:^29-34

a) Infrastructure and funding limitations:

AI adoption in nurse-led clinics is limited by inadequate infrastructure and funding, especially in decentralized or community settings. These clinics often lack secure data systems, advanced hardware, and reliable connectivity essential for AI deployment. High initial costs for technology, training, and maintenance, combined with funding models that prioritize hospital-based innovations, further hinder implementation. Sustainable integration requires targeted policy support, equitable investment, and infrastructure designed for frontline nursing environments.

b) Resistance to change in healthcare settings:

Resistance to change is a key barrier to AI integration in nurse-led clinics, driven by concerns over role displacement, reduced autonomy, and workflow disruption. Exclusion from AI design and prior experiences with unsupported reforms can intensify skepticism. In relational care settings, AI may be viewed as impersonal or misaligned with nursing values. Inclusive engagement, effective change management, and clear demonstration of AI’s supportive role are critical to building trust and ensuring sustained adoption.

c) Integration with electronic health records (EHRs):

AI integration with Electronic Health Records (EHRs) remains a major barrier in nurse-led clinics due to limited interoperability and outdated digital infrastructure. Many such clinics rely on basic or fragmented systems that lack the real-time data exchange and standardization required for effective AI functionality. Inconsistent documentation, duplicate data entry, and workflow disruptions further hinder adoption. These challenges reduce both the technical efficacy and perceived value of AI tools. Overcoming them requires investment in interoperable EHR systems, standardized data protocols, and AI solutions tailored to nursing workflows.

d) Policy and regulatory gaps:

Policy and regulatory gaps hinder AI adoption in nurse-led clinics by offering limited guidance tailored to nursing-led care models. Existing frameworks are often physician-centric, lacking clarity on data governance, clinical validation, accountability, and legal liability specific to nurses. The absence of standardized accreditation for AI tools further deters implementation. To enable safe and ethical integration, regulations must be adapted to support the unique roles, workflows, and responsibilities within nurse-led settings.

Future Directions and Research Priorities ^35-40

a) AI-human collaboration models in nursing:

Future directions for AI in nurse-led clinics emphasize developing AI-human collaboration models that enhance clinical judgment and support shared decision-making. Research should focus on interpretable, trustworthy systems tailored to nursing workflows in assessment, triage, and care planning. Co-design with nurses is vital to ensure usability and ethical alignment. As highlighted by Topaz and Pruinelli (2020), successful integration requires mutual adaptation between AI tools and nursing practice to improve patient outcomes.

b) Personalized and predictive care led by nurses:

Personalized and predictive nurse-led care represents a shift toward precision healthcare, where AI-driven insights enable early risk detection and tailored interventions. Predictive analytics empower nurses to proactively manage chronic conditions and customize care plans, improving outcomes and promoting efficiency. Such tools enhance clinical autonomy and reinforce nurses’ roles in delivering data-informed, patient-centred care.

c) Research gaps and potential pilot projects.

Future directions for AI in nurse-led clinics highlight research gaps in real-world effectiveness, ethical integration, and impacts on care quality and equity. Limited evidence exists on AI’s performance in decentralized nursing settings and its influence on clinical decisions and patient engagement. Pilot studies should evaluate AI-driven triage, remote monitoring, and risk prediction within nurse-led models, emphasizing usability, outcomes, and stakeholder acceptability. Context-specific trials are crucial to inform ethical and scalable AI adoption in frontline nursing care.

d) Policy implications and scalability:

Future research on AI in nurse-led clinics should address policy and scalability challenges to support sustainable, equitable integration. Key gaps include the absence of regulatory frameworks, data governance standards, and reimbursement models suited to nurse-led settings. Research should identify policy enablers that ensure privacy, foster collaboration, and promote safe AI use. Scalable pilot studies must evaluate regulatory compliance, interoperability, and cost-effectiveness across diverse care contexts. Aligning AI innovation with health policy is vital for responsible, widespread adoption in frontline nursing care.

e) Nursing Education and Training in the Age of AI

In the AI-driven healthcare landscape, integrating informatics competencies into nursing education is essential for preparing nurses to make data-informed decisions, interact effectively with AI systems, and uphold data security while delivering high-quality care. As underscored by the TIGER initiative (Sensmeier, 2020), such competencies are foundational for safe and professional technology-enabled practice. To meet these demands, nursing curricula must be reformed to embed digital health, informatics, and data literacy, supported by continuous professional development (CPD) programs that enhance adaptability and lifelong learning. The World Health Organization (2021) advocates for competency-based education and sustained training to build a resilient, digitally competent nursing workforce. Nurse educators and leaders play a pivotal role in this transformation by driving curriculum innovation, fostering informatics and critical thinking skills, and ensuring ethical integration of technology, with the International Council of Nurses (2021) affirming the importance of strong academic leadership in preparing nurses for future-ready, AI-integrated clinical practice.

CONCLUSION:

Artificial intelligence has the potential to significantly enhance nurse-led clinics by improving efficiency, decision-making, and access to personalized care, especially in underserved settings. However, its integration requires careful attention to ethical, educational, infrastructural, and regulatory challenges. A nurse-informed, equity-driven approach is essential to ensure safe, effective, and sustainable adoption of AI in frontline nursing practice.

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

Received on 10.11.2025 Revised on 23.12.2025

Accepted on 28.01.2026 Published on 26.02.2026

Available online from March 03, 2026

A and V Pub J. of Nursing and Medical Res. 2026;5(1):43-48.

DOI: 10.52711/jnmr.2026.10

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.