ArticleFrontiers in oncology2024
Prospective study of AI-assisted prediction of breast malignancies in physical health examinations: role of off-the-shelf AI software and comparison to radiologist performance.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the radiologic assessment of ductal carcinomaFrontiers in oncology · 2026Pooled it
- Comparison of artificial intelligence (AI) services for Breast Imaging-Reporting and Data System (BI-RADS) classification on mammograms.Quantitative imaging in medicine and surgery · 2026Article
- Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.JMIR formative research · 2026Article
- Evaluating the diagnostic performance of artificial intelligence-assisted decision-making software for pulmonary nodules in a resource-limited setting.Journal of clinical imaging science · 2026Article
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8 authors.
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Abstract
Objective: In physical health examinations, breast sonography is a commonly used imaging method, but it can lead to repeated exams and unnecessary biopsy due to discrepancies among radiologists and health centers. This study explores the role of off-the-shelf artificial intelligence (AI) software in assisting radiologists to classify incidentally found breast masses in two health centers. Methods: Female patients undergoing breast ultrasound examinations with incidentally discovered breast masses were categorized according to the 5 Results: A total of 196 patients with 202 breast masses were included in the study, with pathological results confirming 107 benign and 95 malignant masses. The receiver operating characteristic curve showed that experienced breast radiologists had higher diagnostic performance in BI-RADS classification than junior radiologists, similar to AI classification (AUC = 0.936, 0.806, 0.896, and 0.950, Conclusions: The AI software enhances diagnostic efficiency for breast masses, reducing the performance gap between junior and senior radiologists, particularly for BI-RADS 4a and 4b masses. This improvement reduces unnecessary repeat examinations and biopsies, optimizing medical resource utilization and enhancing overall diagnostic effectiveness.
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