Certain breast cancer types, like luminal cancer and those in dense breasts, are more likely to be missed by AI in mammography screening.
Key Details
- 1South Korean researchers reviewed breast images from nearly 1,100 women screened between 2014 and 2020.
- 2AI tools were found to miss cancers with features such as luminal subtype, dense breast tissue, and lesions outside the mammary zone.
- 3The study was published in RSNA's journal Radiology.
- 4Experts urge careful assessment of cases at higher risk of AI error.
Why It Matters
Understanding which cancers AI is most likely to miss can help optimize AI integration into breast imaging workflows, ensuring that radiologists remain vigilant in specific high-risk contexts. This research offers guidance to improve both detection and patient outcomes.
Source
Radiology Business
Related News
•Radiology Business
Framework Assesses Real-World Financial Impact of Radiology AI Adoption
A new analysis presents a financial calculator for objectively assessing the return on investment (ROI) of implementing radiology AI solutions.
•Radiology Business
AI Technique Unveils Previously Hidden MS Gray Matter Lesions on MRI
Researchers developed an AI-enhanced method to detect previously invisible gray matter lesions in multiple sclerosis using MRI.
•Radiology Business
Majority of Patients Want Disclosure When AI Used in Imaging
A new survey finds that nearly all patients want to be informed when AI is utilized in medical imaging interpretation.