Evidence map›Paper›PMID 35492311›Full record

ArticleFrontiers in medicine2022

Endoscopists' Acceptance on the Implementation of Artificial Intelligence in Gastrointestinal Endoscopy: Development and Case Analysis of a Scale.

Li Tian, Zinan Zhang, Yu Long, Anliu Tang, Minzi Deng, Xiuyan Long, Ning Fang, Xiaoyu Yu, Xixian Ruan, Jianing Qiu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2023
    Article
  5. 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

12 authors.

Li TianDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Zinan ZhangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Yu LongHealth Management Center, The Third Xiangya Hospital of Central South University, Changsha, China.
Anliu TangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Minzi DengDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Xiuyan LongDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Ning FangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Xiaoyu YuDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Xixian RuanDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Jianing QiuDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Xiaoyan WangDepartment of Gastroenterology, The Third Xiangya Hospital of Central South University, Changsha, China.
Haijun DengDepartment of Public Health, Yangtze University, Jingzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The purpose of this paper is to develop and validate a standardized endoscopist acceptance scale for the implementation of artificial intelligence (AI) in gastrointestinal endoscopy. Methods: After investigating endoscopists who have previously used AI and consulting with AI experts, we developed a provisional scale to measure the acceptance of AI as used in gastrointestinal endoscopy that was then distributed to a sample of endoscopists who have used AI. After analyzing the feedback data collected on the provisional scale, we developed a new formal scale with four factors. Cronbach's alpha, confirmatory factor analysis (CFA), content validity, and related validity were conducted to test the reliability and validity of the formal scale. We also constructed a receiver operating characteristic (ROC) curve in order to determine the scale's ability to distinguish higher acceptance and satisfaction. Results: A total of 210 valid formal scale data points were collected. The overall Cronbach's alpha was 0.904. All the factor loadings were >0.50, of which the highest factor loading was 0.86 and the lowest was 0.54 (AVE = 0.580, CR = 0.953). The correlation coefficient between the total score of the scale and the satisfaction score was 0.876, and the area under the ROC curve was 0.949 ± 0.031. Endoscopists with a score higher than 50 tend to be accepting and satisfied with AI. Conclusion: This study yielded a viable questionnaire to measure the acceptance among endoscopists of the implementation of AI in gastroenterology.

Indexed as

analysisartificial intelligenceendoscopistgastrointestinal endoscopyscale

Identifiers

PMID35492311
PMCPMC9040450

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Registered trials

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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.