AI misses 14% of invasive breast cancers on mammography, with luminal cancers and dense tissue posing significant challenges.
Key Details
- 1Study analyzed 1,097 breast cancers from 1,082 women using Lunit Insight MMG AI between 2014–2020.
- 2AI missed 154 cancers (14%), often in younger women with smaller (<2 cm), lower-grade tumors and dense breasts.
- 3Missed cancers were more likely luminal subtype (false-negative rate 17.2%), compared to HER2-enriched (9%) and triple-negative (14.5%).
- 4Major reasons for AI misses included dense breast tissue (n=56), nonmammary zone location (n=22), architectural distortion (n=12), and amorphous calcifications (n=5).
- 561.7% of AI-missed cancers were deemed 'actionable' on further review by radiologists.
Why It Matters
Understanding which cancer features are often overlooked by AI tools enables radiologists to better interpret AI results, reducing missed diagnoses. These insights highlight the continued importance of human expertise in mammography, especially for high-risk groups like women with dense breast tissue.
Source
AuntMinnie
Related News
•AuntMinnie
Stanford Team Introduces Real-Time AI Safety Monitoring for Radiology
Stanford researchers introduced an ensemble monitoring model to provide real-time confidence assessments for FDA-cleared radiology AI tools.
•AuntMinnie
Head-to-Head Study Evaluates AI Accuracy in Fracture Detection on X-Ray
A prospective study compared three commercial AI tools for fracture detection on x-ray, showing moderate-to-high accuracy for simple cases but weaker performance in complex scenarios.
•AuntMinnie
AI Boosts Agreement in CAD-RADS Classification on Cardiac CT
Deep learning AI improves interreader agreement in CAD-RADS assessments on coronary CT angiography.