Implementation of artificial intelligence in the nursing profession: Barriers & facilitators

Article Type : Correspondence


1 Department of Medical-Surgical, Amol Faculty of Nursing and Midwifery, Mazandaran University of Medical Sciences, Sari, Iran

2 Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 Department of Medical-Surgical Nursing, Kashan University of Medical Sciences, Kashan, Iran

To the Editor

Nurses make up the largest group of healthcare professionals worldwide and are therefore more likely to be heavily exposed to artificial intelligence (AI) technologies due to their significant workforce [1, 2]. The utilization of AI techniques in nursing is being implemented to improve decision-making processes, advance patient care, and optimize the delivery of services [3-5]. Considering the wide use of AI in this profession, there are several barriers and challenges in this path, which can be facilitated by removing these barriers. Some of these barriers are changing the intrinsic nature of healthcare delivery, ethical risks, the validity of evidence, the fairness of outcomes, and the traceability of harm caused by algorithmic activity [6]. Nursing technologies currently in use gather and utilize healthcare data that can anticipate future events that have the potential to obstruct the delivery of care [7]. The precise and safe development and deployment of AI in mental health nursing requires careful consideration of the nuances of clinical complexities, while also ensuring adherence to ethical nursing principles [8]. AI can harm the patient-nurse communication. However, AI has potential benefits to serve patients and improve care delivery and patients’ outcomes [9]. Robot nurses and AI applications have been shown to alleviate the burden on nurses, improve the quality of patient care, and reduce the potential for medical errors and malpractice [10].

In general, it is recommended that nursing policy makers and managers pay special attention to the implementation of AI to improve the quality of nursing care. Also, it is suggested that researchers in the nursing profession design well-designed studies to evaluate the implications of implementing AI to improve the quality of nursing care.



Not applicable.


Authors’ contributions

Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: MHG, AZK, ZA, MJG; Drafting the work or revising it critically for important intellectual content: MHG, AZK, ZA, MJG; Final approval of the version to be published: MHG, AZK, ZA, MJG; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: MHG, AZK, ZA, MJG.





Ethics approval and consent to participate

Not applicable.


Competing interests

We do not have potential conflicts of interest with respect to the research, authorship, and publication of this article.


Using artificial intelligent chatbots



This is an open access article under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (CC BY-NC 4.0).

© 2023 The Author(s).

  1. Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs. 2021;77(9):3707-3717.
  2. Mobayen M, Ghazanfari MJ, Feizkhah A, Emami Zeydi A, Karkhah S. Machine learning for burns clinical care: Opportunities & challenges. Burns. 2022;48(3):734-735.
  3. O'Connor S, Yan Y, Thilo FJ, Felzmann H, Dowding D, Lee JJ. Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs. 2023;32(13-14):2951-2968.
  4. Feizkhah A, Mobayen M, Ghazanfari MJ, Bagheri Toolaroud P, Ghorbani Vajargah P, Mollaei A, et al. Machine learning for burned wound management. Burns. 2022;48(5):1261-1262.
  5. Javadi-Pashaki N, Ghazanfari MJ, Karkhah S. Machine Learning for Geriatric Clinical Care: Opportunities and Challenges. Ann Geriatr Med Res. 2021;25(2):137-138.
  6. Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, et al. Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res. 2021;23(11):e26522.
  7. Carroll W. Artificial intelligence, nurses and the quadruple aim. Online J Nurs Inform. 2018;22(2):1-2.
  8. Wilson RL, Higgins O, Atem J, Donaldson AE, Gildberg FA, Hooper M, et al. Artificial intelligence: An eye cast towards the mental health nursing horizon. Int J Ment Health Nurs. 2023;32(3):938-944.
  9. Peltonen L-M, Pruinelli L, Lewis A, Block L, Topaz M, Von Gerich H, et al. Will artificial intelligence replace nurses? A debate. Stud Health Technol Inform. 2021;284:341-343.
  10. Ergin E, Karaarslan D, Şahan S, Çınar Yücel Ş. Artificial intelligence and robot nurses: From nurse managers' perspective: A descriptive cross‐sectional study. J Nurs Manag. 2022;30(8):3853-3862.
Volume 2, Issue 1
January 2024
Pages 47-48
  • Receive Date: 22 April 2023
  • Revise Date: 13 June 2023
  • Accept Date: 15 June 2023
  • First Publish Date: 25 June 2023
  • Publish Date: 25 June 2023