Evidence map›Paper›PMID 37133213›Full record

ArticleKorean journal of radiology2023

A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea.

Hyunsu Choi, Leonard Sunwoo, Se Jin Cho, Sung Hyun Baik, Yun Jung Bae, Byung Se Choi, Cheolkyu Jung, Jae Hyoung Kim

Abstract read
In one paragraph

Article in Korean journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

8 authors.

Hyunsu ChoiDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0002-5801-8882
Leonard SunwooDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0003-0374-8658
Se Jin ChoDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0001-6450-7554
Sung Hyun BaikDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0003-3738-8988
Yun Jung BaeDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0002-1779-4949
Byung Se ChoiDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0001-6310-1798
Cheolkyu JungDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0002-8862-7347
Jae Hyoung KimDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Korea.ORCID 0000-0002-0545-4138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND

methodsIn April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses.

resultsThe survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81];

conclusionA majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

Indexed as

Artificial IntelligenceSoftwareHumansInternetRadiologistsRepublic of KoreaSurveys and QuestionnairesArtificial intelligenceNeuroradiologyPerceptionsUser experienceWeb-based survey

Identifiers

PMID37133213
PMCPMC10157324

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.