ArticleEuropean radiology2026
AI-CAD for diagnostic mammography: comparison to radiologists according to different indications.
Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Long-term outcomes of ventricular entry in high grade glioma resection: A meta-Analysis and meta regression.Neurosurgical review · 2026Pooled it
- IgeaNet: a deep learning model for opportunistic osteoporosis screening from chest X-ray images.La Radiologia medica · 2026Article
- Higher cerebral metabolic rate of oxygen derived from dynamic susceptibility contrast MRI is an independent predictor of poor survival in IDH-wildtype glioblastoma.Neuroradiology · 2026Article
- Performance of a Screening Mammography AI Algorithm Repurposed for Symptomatic Mammography in a Tertiary Outpatient Clinic.Diagnostics (Basel, Switzerland) · 2026Article
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
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Abstract
objectiveAlthough artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly applied in screening mammography, its use in diagnostic settings is less established. This study evaluated the diagnostic performance of AI-CAD abnormality scores at optimized thresholds across various diagnostic indications vs radiologists. MATERIALS AND
methodsThis retrospective study included 1534 women (mean age, 51.4 ± 8.8 years) who underwent diagnostic mammography between March 2015 and February 2016. Cases were categorized into three diagnostic indications: (1) symptomatic, (2) BI-RADS 3 follow-up, and (3) referral for abnormal imaging. A commercially available AI-CAD system provided abnormality scores (0-100%). Final diagnosis was confirmed by pathology or ≥ 2-year imaging stability. AI-CAD performance (sensitivity, specificity, accuracy, PPV, and AUC was evaluated at two thresholds: vendor-recommended 10% for screening and an optimized 50% from ROC analysis (Youden's index), and compared with original radiologist interpretations.
resultsAmong the 1534 patients, 397 (25.9%) were diagnosed with breast cancer. At the 50% threshold, AI-CAD showed significantly higher specificity (95.0% vs 86.2%), accuracy (91.7% vs 87.2%), and PPV (85.1% vs 69.5%) than radiologists (all p < 0.001). AUCs were comparable (AI-CAD: 0.886; radiologists: 0.882; p = 0.75). In symptomatic patients, AUC was significantly higher than radiologists (0.873 vs 0.815, p = 0.002); in BI-RADS 3 follow-ups and asymptomatic imaging-detected abnormalities, specificity was improved with a tradeoff in lower sensitivity.
conclusionAI-CAD demonstrated diagnostic performance comparable to radiologists in mammography and, at an optimized threshold, offered superior specificity, PPV, and accuracy. Especially in symptomatic patients, a higher threshold increased diagnostic performance without compromising sensitivity. KEY POINTS: Question AI-CAD has the potential to be applied for diagnostic mammography by applying different thresholds. Findings Using an optimized threshold, AI-CAD demonstrated higher specificity, accuracy, and positive predictive value compared to radiologists. Clinical relevance When an optimized threshold is applied, AI-CAD shows comparable performance to radiologists, with higher specificity, accuracy, and positive predictive value.
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