Evidence map›Paper›PMID 40598077›Full record

ArticleBMC nursing2025

Exploring the experiences and perceptions of nursing students in utilizing artificial intelligence: a descriptive phenomenological study.

Maysam Safari Nezhad, Alireza Abdi, Mahnaz Ahmadi

Abstract read
In one paragraph

Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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

3 authors.

Maysam Safari NezhadStudent Research Committee, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Alireza AbdiDepartment of Emergency and Critical Care Nursing, Nursing and Midwifery School, Social Development & Health Promotion Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran. A_abdi61@yahoo.com.
Mahnaz AhmadiStudent Research Committee, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence is transforming healthcare by enhancing diagnostics, efficiency, and outcomes. Evaluating nursing students' knowledge helps identify educational gaps, as they directly engage in patient care and decision-making. This study explored their experiences with using artificial intelligence in clinical settings.

methodsA descriptive phenomenological approach was employed to recruit eight nursing students from the School of Nursing and Midwifery in Kermanshah-Iran, all of whom had prior experience with artificial intelligence applications in clinical settings. Participants were selected purposefully and underwent in-depth, semi-structured interviews. Eight interview sessions lasting 60-70 min each were conducted, with data saturation achieved after the eighth interview. The data were analyzed using Colaizzi's seven-step method, ensuring adherence to Guba and Lincoln's criteria for trustworthiness throughout the research process. Data management was facilitated using MAXQDA 20 software.

resultsData analysis yielded 450 initial codes, 5 sub-themes, and 4 themes. The themes identified were: (1) Novel Potentials and Broad Horizons of Artificial Intelligence, (2) Ethical and Security Challenges In The World of Artificial Intelligence, (3) Changes in Human Identity and Social Relationships In The Age of Artificial Intelligence, and (4) Enhancing and Reinventing Skills for Coexistence With Artificial Intelligence.

conclusionThe study revealed that nursing students recognize the potential of artificial intelligence to enhance precision and efficiency in patient care. However, they emphasized the need for further education and the removal of barriers to its effective implementation. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Artificial intelligenceLife experiencesNursing studentsQualitative research

Identifiers

PMID40598077
PMCPMC12210510

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LicenceCC BY-NC-ND
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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.