Incorporation of Artificial Intelligence into Nursing Research: A Scoping Review

Overview

Journal Int Nurs Rev

Specialty Nursing

Date 2024 Jul 5

PMID 38967044

Authors

Yasin M Yasin

Areej Al-Hamad

Kateryna Metersky

Vahe Kehyayan

Affiliations

Soon will be listed here.

Abstract

Background: The integration of artificial intelligence (AI) across different sectors, notably healthcare, is on the rise. However, a thorough exploration of AI's incorporation into nursing research, as well as its advantages and obstacles, is still lacking.

Objective: The aim of this scoping review was to map the roles, benefits, challenges, and potentials for the future development and use of AI in the context of nursing research.

Methods: An exhaustive search was conducted across seven databases: MEDLINE, PsycINFO, SCOPUS, Web of Science, CINAHL, Google Scholar, and ProQuest. Articles were additionally identified through manual examination of reference lists of the articles that were included in the study. The search criteria were restricted to articles published in English between 2010 and 2023. The Joanna Briggs Institute (JBI) approach for scoping reviews and the PRISMA-ScR guidelines guided the processes of source selection, data extraction, and data presentation.

Results: Twenty articles met the inclusion criteria, covering topics from ethical considerations to methodological issues and AI's capabilities in data analysis and predictive modeling.

Conclusion: The review identified both the potentials and complexities of integrating AI into nursing research. Ethical and legal considerations warrant a coordinated approach from multiple stakeholders.

Implication: The findings emphasized AI's potential to revolutionize nursing research, underscoring the need for ethical guidelines, equitable access, and AI literacy training to ensure its responsible and inclusive use.

Citing Articles

Incorporation of artificial intelligence into nursing research: A scoping review.

Yasin Y, Al-Hamad A, Metersky K, Kehyayan V Int Nurs Rev. 2024; 72(1):e13013.

PMID: 38967044 PMC: 11741909. DOI: 10.1111/inr.13013.

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