Aug 2026· Genetics and Molecular Research· 0 citations· 5 references
TL;DR
The role of AI in nursing practice, its impact on patient care and clinical decision-making, applications in nursing education, advantages, challenges, ethical considerations, and future implications for the nursing profession are discussed.
Abstract
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, improving patient safety, streamlining documentation, and strengthening nursing education. Nursing professionals are increasingly using AI-enabled technologies such as clinical decision-support systems, predictive analytics, virtual assistants, remote patient monitoring, natural language processing, robotics, and intelligent electronic health records. These technologies can assist nurses in identifying patient deterioration, prioritizing care, reducing medication errors, managing large volumes of clinical information, and developing individualized care plans. In nursing education, AI provides opportunities for adaptive learning, virtual simulation, automated assessment, personalized feedback, and development of clinical reasoning skills. However, successful implementation requires attention to ethical issues, data privacy, algorithmic bias, cybersecurity, professional accountability, technological literacy, and the preservation of human-centered care. Nurses must therefore develop appropriate AI literacy while maintaining critical thinking and clinical judgment. This article discusses the role of AI in nursing practice, its impact on patient care and clinical decision-making, applications in nursing education, advantages, challenges, ethical considerations, and future implications for the nursing profession.
Artificial intelligence (AI) is rapidly transforming healthcare delivery and nursing education by supporting clinical decision-making, documentation, risk prediction, personalized learning, simulation, assessment, and administrative processes. The increasing availability of generative AI and large language models has f...
Natasha Mohit Mahajan· International Journal of Nur...· 0 citations
The evidence indicates that AI can improve risk stratification, early recognition of clinical deterioration, medication-safety processes, documentation quality, and access to simulation-based education, and support a human-in-the-loop model in which AI augments rather than replaces nursing judgment.
Heloísa de Oliveira Nascimento· Nexus Science Review· 0 citations
Background: Artificial intelligence-based clinical decision support systems (AI-CDSS) are increasingly used in healthcare to support data-informed clinical decisions. In nursing practice, these systems may assist with risk assessment, medication safety, early recognition of patient deterioration, documentation, and car...
I. M, Bamigboye T. O., Oniyide A. A.· International journal of re...· 0 citations
Effective use of AI-based decision support requires more than technical familiarity; it also demands digital literacy, critical appraisal, and the ability to contextualize AI outputs within individual patient and healthcare settings, which presents both a gap and an opportunity for medical and allied health education.
Darin Jan C. Tindowen, M. L. Batugal, R. J. Acupido et al.· Journal of Primary Care & Co...· 0 citations
GenAI is a useful tool supporting the workflow of clinical nurse specialists in all practice settings and can build stronger nursing and healthcare systems resources and optimize patient outcomes.
Andrew J Salimbeni, M. Makic, S. Reed· Clinical nurse specialist CN...· 0 citations
Sources on AI literacy, responsiveness, and related nursing concepts were thematically synthesized and four defining attributes were identified: foundational understanding of AI, critical appraisal of AI outputs, ethical and accountable response, and integration with clinical judgment.
Areej Al-Hamad, Y. Yasin, Lujain Yasin· ANS. Advances in nursing sci...· 0 citations
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