ArticleUltrasonography (Seoul, Korea)2022
Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level.
Article in Ultrasonography (Seoul, Korea), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effect of an annotation-free artificial intelligence system for simultaneous detection and diagnosis on breast ultrasonography: a multireader, multicase study across diverse healthcare professionals.Ultrasonography (Seoul, Korea) · 2026Article
- Feasibility of Using an AI System for Breast Ultrasonography Interpretation According to Clinical Expertise: Results of a Pilot Study.Journal of the Korean Society of Radiology · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Artificial intelligence-enhanced handheld breast ultrasound for screening: A systematic review of diagnostic test accuracy.PLOS digital health · 2025Article
- Utilization of artificial intelligence to triage patients with delayed follow-up of probably benign breast ultrasound findings.Ultrasonography (Seoul, Korea) · 2025Article
- Improving breast ultrasonography education: the impact of AI-based decision support on the performance of non-specialist medical professionals.Ultrasonography (Seoul, Korea) · 2025Article
- Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study.Gland surgery · 2024Article
- Diagnostic performance of deep learning in ultrasound diagnosis of breast cancer: a systematic review.NPJ precision oncology · 2024Review
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Authors and funding
9 authors.
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Abstract
purposeThis study evaluated how artificial intelligence-based computer-assisted diagnosis (AICAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows.
methodsImages of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD.
resultsAfter implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists.
conclusionUsing AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.
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