Evidence map›Paper›PMID 40611396›Full record

ArticleJournal of gastroenterology and hepatology2025

A Model Predicting Artificial Intelligence Use by Gastroenterology Nurses in Clinical Practice: A Cross-Sectional Multicenter Survey.

Thomas Yuen Tung Lam, Yue Hu, Y Yi, Peter J Schulz, May O Lwin, Kalya M Kee, Wilson W B Goh, Max F K Cheung, H S Lee, Alice S H Fan and 10 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of gastroenterology and hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. Article
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

20 authors.

Thomas Yuen Tung LamThe Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-4306-4990
Yue HuThe Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0002-8791-4512
Y YiStanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0009-0001-7522-8486
Peter J SchulzWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.
May O LwinWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.
Kalya M KeeWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.
Wilson W B GohLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Max F K CheungLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
H S LeeCombined Endoscopy Unit, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China.
Alice S H FanCombined Endoscopy Unit, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China.
Phyllis P Y LamHui Siu Wing Endoscopy Centre, CUHK Medical Centre, The Chinese University of Hong Kong, Hong Kong SAR, China.
S F LamShaw Endoscopy Centre, Prince of Wales Hospital, Hong Kong SAR, China.
L ZhouDepartment of Gastroenterology, The National Key Clinical Specialty, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Y ChenDepartment of Gastroenterology, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
F LiDepartment of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Changchun, China.ORCID https://orcid.org/0000-0002-2564-7826
Ying LauThe Nethersole School of Nursing, The Chinese University of Hong Kong, Hong Kong SAR, China.
Jer-Wei WuDepartment of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Han-Mo ChiuDepartment of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.ORCID https://orcid.org/0000-0003-2786-8056
H XuDepartment of Gastroenterology and Endoscopy Center, First Hospital of Jilin University, Changchun, China.ORCID https://orcid.org/0000-0003-2798-2078
Joseph J Y SungInstitute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.ORCID https://orcid.org/0000-0003-3125-5199

Funding

The Chinese University of Hong Kong
6 · The paper itself

Abstract

BACKGROUND AND

aimsNurses' participation during colonoscopy has been demonstrated to significantly improve the detection rate of polyps and adenomas. Nonetheless, the adoption of AI in clinical practice still poses challenges. There is limited understanding of the factors influencing gastroenterology nurses' intentions to use AI in clinical practice. We aimed to examine how gastroenterology nurses' intentions to use AI are affected by perceived usefulness, acceptance of this technology, and perceived risk via a moderated mediation model controlling for nurses' characteristics.

methodsA cross-sectional multicenter survey study was conducted among gastroenterology nurses from 54 hospitals in Taiwan, Hong Kong, and mainland China. A total of 337 nurses (mean age 37.40 ± 8.29 years, 81.6% females) completed the survey.

resultsAfter controlling for previous experience with AI, number of working years, and work role, a statistically significant direct effect of perceived usefulness on use intention was found. The indirect effect of perceived usefulness on use intention through AI technology acceptance was the most robust when perceived risk was at the lowest level.

conclusionsFindings suggest that perceived usefulness facilitated the intentional use of AI in clinical practice through acceptance of AI, especially when perceived risk was low.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelColonoscopyGastroenterologyNursesAdultChinaCross-Sectional StudiesFemaleHumansIntentionMaleMiddle AgedSurveys and QuestionnairesTaiwanartificial intelligencegastroenterologynursesrisk

Identifiers

PMID40611396
PMCPMC12400256

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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.