Gangolu Harsha
- Nursing Tutor All India Institute of Medical Sciences (AIIMS) Andhra Pradesh India
Abstract
Artificial intelligence (AI) is rapidly transforming the healthcare industry, and nursing professionals stand to benefit significantly from its integration. This article explores the role of AI in revolutionizing nursing, highlighting its applications, benefits, challenges, and ethical considerations. Drawing on examples from countries actively integrating AI into nursing services such as the United States, the United Kingdom, China, and Japan, the article discusses how AI supports nursing tasks, enhances patient care, and improves healthcare outcomes. Key benefits include early warning systems, predictive analytics, remote monitoring, triage support, and workforce management. Stakeholders play crucial roles in encouraging AI adoption, from education and advocacy to research and policy development. However, challenges such as data privacy, algorithm bias, and ongoing training requirements must be addressed. Ethical considerations related to bias, fairness, and transparency in AI decision-making also require attention. Ultimately, embracing AI’s potential while navigating its challenges is essential for advancing nursing practice and improving patient care in the future of healthcare.
Keywords: Artificial intelligence, nursing, healthcare, revolution, patient care, predictive analytics, remote monitoring, triage, stakeholders, challenges, ethical considerations
[This article belongs to Journal of Nursing Science & Practice(jonsp)]
References
- Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019; 6 (2): 94–98.
- Wilbanks BA, Langford PA. Artificial intelligence in nursing: an integrative review. Nurs Educ Perspect. 2019; 40 (2): 108–113.
- Rahul K, Banyal RK, Arora N. A systematic review on big data applications and scope for industrial processing and healthcare sectors. J Big Data. 2023;10:133. doi: 10.1186/s40537-023-00808-2.
- Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25 (1): 44–56.
- Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017; 2 (4): 230–243.
- Radhakrishnan K, Aswath A. Artificial intelligence: the future of healthcare. J Res Pharm Pract. 2018; 7 (4): 107–110.
- Koo LW, Horng MF, Yang CT. Potential of artificial intelligence in patient care. JAMA Surg. 2019; 154 (4): 328–329.
- Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542 (7639): 115–118.
- Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019; 380 (14): 1347–1358.
- Zikos D, Diomidous M, Zikos A, Prezerakos P. Implementation of AI in nursing practice: a case study. Stud Health Technol Inform. 2020; 272: 272–275.
- Long E, Lin H, Liu Z, Wu X. Artificial intelligence in healthcare: anticipating challenges. Am Med Assoc J Ethics. 2018; 20 (10): E941–E946.
- Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, Qiu Y. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res. 2021;49 (3): 3000605211000157.
- Jaremko JL, Azar M, Bromwich R, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019; 70: 107–118.
- Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. J Am Coll Radiol. 2019; 16: 1516–1521.
Journal of Nursing Science & Practice
| Volume | 14 |
| Issue | 01 |
| Received | January 2, 2024 |
| Accepted | February 22, 2024 |
| Published | February 29, 2024 |