Evidence map›Paper›PMID 34406963›Full record

ArticleJMIR nursing2020

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review.

Christine Buchanan, M Lyndsay Howitt, Rita Wilson, Richard G Booth, Tracie Risling, Megan Bamford

Open access · goldAbstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR nursing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 7 pooled it
11.5field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

84 citing papers in PubMed, 7 syntheses or guidelines pooled it, 198 citations in OpenAlex.

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  4. The Role of AI in Nursing Education and Practice: Umbrella Review.Journal of medical Internet research · 2025
    Pooled it
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  6. AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024
    Pooled it
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24 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Christine BuchananRegistered Nurses' Association of Ontario, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-5053-0737
M Lyndsay HowittRegistered Nurses' Association of Ontario, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-6424-2290
Rita WilsonRegistered Nurses' Association of Ontario, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-3461-736X
Richard G BoothArthur Labatt Family School of Nursing, Western University, London, ON, Canada.ORCID https://orcid.org/0000-0002-0300-9954
Tracie RislingCollege of Nursing, University of Saskatchewan, Saskatoon, SK, Canada.ORCID https://orcid.org/0000-0002-4235-4785
Megan BamfordRegistered Nurses' Association of Ontario, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2346-1606
Registered Nurses' Association of Ontario · CAUniversity of Saskatchewan · CAWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is set to transform the health system, yet little research to date has explored its influence on nurses-the largest group of health professionals. Furthermore, there has been little discussion on how AI will influence the experience of person-centered compassionate care for patients, families, and caregivers.

objectiveThis review aims to summarize the extant literature on the emerging trends in health technologies powered by AI and their implications on the following domains of nursing: administration, clinical practice, policy, and research. This review summarizes the findings from 3 research questions, examining how these emerging trends might influence the roles and functions of nurses and compassionate nursing care over the next 10 years and beyond.

methodsUsing an established scoping review methodology, MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Center, Scopus, Web of Science, and ProQuest databases were searched. In addition to the electronic database searches, a targeted website search was performed to access relevant gray literature. Abstracts and full-text studies were independently screened by 2 reviewers using prespecified inclusion and exclusion criteria. Included articles focused on nursing and digital health technologies that incorporate AI. Data were charted using structured forms and narratively summarized.

resultsA total of 131 articles were retrieved from the scoping review for the 3 research questions that were the focus of this manuscript (118 from database sources and 13 from targeted websites). Emerging AI technologies discussed in the review included predictive analytics, smart homes, virtual health care assistants, and robots. The results indicated that AI has already begun to influence nursing roles, workflows, and the nurse-patient relationship. In general, robots are not viewed as replacements for nurses. There is a consensus that health technologies powered by AI may have the potential to enhance nursing practice. Consequently, nurses must proactively define how person-centered compassionate care will be preserved in the age of AI.

conclusionsNurses have a shared responsibility to influence decisions related to the integration of AI into the health system and to ensure that this change is introduced in a way that is ethical and aligns with core nursing values such as compassionate care. Furthermore, nurses must advocate for patient and nursing involvement in all aspects of the design, implementation, and evaluation of these technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/17490.

Indexed as

artificial intelligencemachine learningnursingpatient-centered carereviewrobotics

Identifiers

PMID34406963
PMCPMC8373374
OpenAlexW3095399468

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.