Evidence map›Paper›PMID 41929405›Full record

ArticleFrontiers in radiology2026

Artificial intelligence-assisted breast ultrasound: modest AUROC improvement and shorter interpretation time without significant change in diagnostic accuracy.

Hyuksool Kwon, Sun Mi Kim, Seok Hwan Oh, Mijung Jang, Myeong-Gee Kim, Hyeon-Min Bae, Sang Il Choi, Su Min Cho, Youngmin Kim, Guil Jung and 2 more

Abstract read
In one paragraph

Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hyuksool KwonLaboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Sun Mi KimDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
Seok Hwan OhLaboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Mijung JangDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
Myeong-Gee KimLaboratory of Quantitative Ultrasound Imaging, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Hyeon-Min BaeBarreleye Inc., Seoul, Republic of Korea.
Sang Il ChoiDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
Su Min ChoDepartment of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
Youngmin KimElectrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Guil JungElectrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Hyeon-Jik LeeElectrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Sang-Yun KimElectrical Engineering Department, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To evaluate whether Vis-BUS, a commercial artificial intelligence (AI) breast ultrasound detection and analysis software, improves diagnostic discrimination and interpretation efficiency in breast ultrasound examinations. Materials and methods: This retrospective multi-reader study included 258 breast ultrasound examinations (129 malignant and 129 benign lesions). Six radiologists independently interpreted all cases without AI and, after a two-week washout, with AI assistance. Diagnostic performance metrics, including the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, sensitivity, and specificity, were compared using multi-reader analysis. Median interpretation time per case was recorded and compared using paired statistical tests. Results: Vis-BUS assistance modestly increased the pooled AUROC (0.921 vs. 0.953, Conclusion: Vis-BUS AI assistance was associated with improved diagnostic discrimination and shorter interpretation time. However, accuracy, sensitivity, and specificity did not differ significantly. These findings suggest potential efficiency benefits, while the clinical impact remains to be confirmed in prospective multi-center studies.

Indexed as

artificial intelligencebreast cancercomputer-aided diagnosisdiagnostic accuracyultrasound imaging

Identifiers

PMID41929405
PMCPMC13039021

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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.