Cited by 86Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareEthics and Social Impacts of AI

The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

Open access
Pailaha, Aprianto Daniel·2023·SAGE Open Nursing

Abstract

Research and development of artificial intelligence (AI)-based technologies systems in healthcare has increased over the past decade, highlighting the strong potential of AI to improve the quality of nursing care. To meet the new demands for nursing care, it is necessary that AI be integrated into nursing science and healthcare setting, especially in nursing care. The current challenge is to transform this expanded set of technology into clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, and economical and personalized care. Along with the potential positive outcomes, AI technology also has unintended consequences that have the potential to negatively impact and adversely affect the nursing profession and the primary purpose of nursing practice in healthcare system. This aimed to explore and discuss the impact of applying AI in nursing science and healthcare system to provide approximate nursing care. Some of the impacts that can be evaluated and seen today in the context of using AI technology systems in the scope of nursing and healthcare are expanding access to quality medical care, improving medical records, and improving the quality of services. The use of AI technology systems also has some issues, such as bias and algorithms, which are drawbacks that need to be considered when evaluating the accuracy of the displayed results.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/23779608231196847

Cites

30 of 33 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The absence of nursing-specific AI frameworks hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.

    “This absence limits evaluation and improvement of AI tools and hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.4,5,7”

    From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [7]

    The Introduction says few AI studies reach real-world use and even fewer evaluate clinical outcomes; the Discussion of Topic identifies gaps in AI outcomes research. These passages support a gap in evaluating AI’s effects, but they do not identify an absence of nursing-specific AI frameworks or say that such an absence hinders evaluation or insight into long-term workflow and patient outcomes. (LLM, conf=0.84)

    Experimental — accuracy not yet measured
  • The absence of nursing-specific AI frameworks limits evaluation and improvement of AI tools.

    “This absence limits evaluation and improvement of AI tools and hinders insight into long-term impacts of nursing-specific AI frameworks on nurse clinical workflow and patient outcomes.4,5,7”

    From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [7]

    The Introduction (page not available) says few AI studies evaluate clinical outcomes, and the Discussion of Topic identifies gaps in AI outcomes research. Neither section identifies an absence of nursing-specific AI frameworks or says that such an absence limits evaluation and improvement of AI tools. Searches for nursing-specific frameworks and their effects on evaluation and clinical workflow found no passage addressing that causal claim. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

1 retracted

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Pailaha, A. D.
Affiliations
Inpatient Department Nurse, General Ward Nursing, Department of Nursing, Siloam Hospitals Agora, Jakarta, Indonesia
Corresponding author
Pailaha, Aprianto Daniel
Venue
SAGE Open Nursing
Published
2023-01
Volume
9
Pages
23779608231196847-23779608231196847
ISSN
2377-9608 (ISSN-L)
OpenAlex
W4386544836
Cited by
85
References
30
Citations by year
2026: 21 · 2025: 33 · 2024: 31
FWCI
3.39
Type
journal article
Language
en
Open access
Locations
  • SAGE Open Nursing (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence in healthcare, nursing care, nursing care quality, individualized care, algorithmic bias, unintended consequences of AI, medical records, health care access, nursing profession

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review