Evidence map›Paper›PMID 39453630›Full record

ArticleJMIR nursing2024

Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education.

Patricia A Ball Dunlap, Martin Michalowski

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

26 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

2 authors.

Patricia A Ball DunlapSchool of Nursing, University of Minnesota, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States, 16126245959.ORCID 0009-0008-4048-0113
Martin MichalowskiSchool of Nursing, University of Minnesota, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States, 16126245959.ORCID 0000-0003-2060-5878

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.

Indexed as

Artificial IntelligenceEducation, NursingEthics, NursingForecastingHumansMachine LearningNursing ResearchAI data ethicsartificial intelligencedata-centric AIdata literacyhealth care AImachine learningnursesnursing informaticsresponsible AI

Identifiers

PMID39453630
PMCPMC11529373

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.