A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next?

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Abstract

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI’s utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

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Most cited references 59

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Is Open Access

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2025)

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Machine Learning: Algorithms, Real-World Applications and Research Directions

Iqbal Sarker (2021)

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

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Author and article information

Journal

Journal ID (nlm-ta): J Multidiscip Healthc

Journal ID (iso-abbrev): J Multidiscip Healthc

Journal ID (publisher-id): jmdh

Title: Journal of Multidisciplinary Healthcare

Publisher: Dove

ISSN (Electronic): 1178-2390

Publication date (Electronic): 12 April 2024

Publication date Collection: 2024

Volume: 17

Pages: 1603-1616

Affiliations

[1 ]Department of Medical Nursing, Faculty of Nursing, Mahidol University , Bangkok, Thailand

[2 ]Department of Nursing Siriraj Hospital, Faculty of Medicine Siriraj Hospital, Mahidol University , Bangkok, Thailand

[3 ]Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University , Bangkok, Thailand

[4 ]Department of Nursing, Prince Sultan Military College of Health Sciences , Dammam, Saudi Arabia

[5 ]Frances Payne Bolton School of Nursing, Case Western Reserve University , Cleveland, OH, USA

[6 ]Department of Public Health Nursing, Faculty of Nursing, Mahidol University , Nakhon Pathom, Thailand

Author notes

Correspondence: Suebsarn Ruksakulpiwat, Email suebsarn25@gmail.com

Author information

Suebsarn Ruksakulpiwat http://orcid.org/0000-0003-2168-5195

Sutthinee Thorngthip http://orcid.org/0009-0007-9393-5081

Atsadaporn Niyomyart http://orcid.org/0000-0002-5460-2934

Chitchanok Benjasirisan http://orcid.org/0000-0001-6465-0721

Lalipat Phianhasin http://orcid.org/0000-0003-1960-5539

Heba Aldossary http://orcid.org/0000-0001-8842-6169

Bootan Hasan Ahmed http://orcid.org/0009-0002-5105-7659

Thanistha Samai http://orcid.org/0000-0002-0791-7108

Article

Publisher ID: 459946

DOI: 10.2147/JMDH.S459946

PMC ID: 11020344

PubMed ID: 38628616

SO-VID: 51b8389d-594a-40df-874a-7eeae847b314

License:

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

History

Date received : 16 January 2024

Date accepted : 05 March 2024

Page count

Figures: 2, Tables: 3, References: 59, Pages: 14

Funding

Funded by: Mahidol University, Thailand;

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Mahidol University, Thailand, supported the Article Processing Charge.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next?

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Abstract

Background

Objective

Methods

Results

Conclusion

Related collections

Artificial Intelligence in Medicine

Most cited references 59

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Dissecting racial bias in an algorithm used to manage the health of populations

Machine Learning: Algorithms, Real-World Applications and Research Directions

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