ArticleUltrasonography (Seoul, Korea)2025
Improving breast ultrasonography education: the impact of AI-based decision support on the performance of non-specialist medical professionals.
Article in Ultrasonography (Seoul, Korea), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.JMIR medical informatics · 2026Article
- 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
- Breast Cancer Screening Programs in China: Implementation, Challenges, and Future Perspectives.Korean journal of radiology · 2026Review
- Ultrasonography in 2026: accelerating progress and defining the future of ultrasound.Ultrasonography (Seoul, Korea) · 2026Article
- The role of AI for improved management of breast cancer: Enhanced diagnosis and health disparity mitigation.Computer methods and programs in biomedicine · 2025Review
- Clinical Application of Artificial Intelligence in Breast Ultrasound.Journal of the Korean Society of Radiology · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThis study evaluated the educational impact of an artificial intelligence (AI)-based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging.
methodsIn this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions.
resultsThe mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120).
conclusionThe mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.
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