Algorithms from the 2023 RSNA Screening Mammography AI Challenge demonstrated strong performance, with leading models achieving high sensitivity and specificity in breast cancer detection.
Key Details
- 1The 2023 RSNA AI Challenge evaluated 1,537 algorithms on an independent dataset of 5,415 women from the US and Australia.
- 2Median recall rate of all algorithms was 1.7%, with the top-performing algorithm at 1.5%.
- 3Top algorithm sensitivity was 48.6% vs. median of 27.6%; specificity was 99.5% vs. 98.7% median.
- 4Ensemble models of the top 3 and top 10 algorithms achieved sensitivities of 60.7% and 67.8%, with corresponding recall rates of 2.4% and 3.5%.
- 5Sensitivity was higher in the Australian evaluation set (68.1%) than the US set (52.0%).
- 6The top models had higher sensitivity for invasive (68.0%) over noninvasive cancers (43.8%).
Why It Matters

Source
AuntMinnie
Related News

Most FDA-Cleared AI Devices Lack Pre-Approval Safety Data, Study Finds
A new study finds fewer than 30% of FDA-cleared AI medical devices reported key safety or adverse event data before approval.

BMI Significantly Impacts AI Accuracy in CT Lung Nodule Detection
New research demonstrates that high BMI negatively impacts both human and AI performance in chest low-dose CT interpretation, highlighting dataset diversity concerns.

Landmark AI Mammography Trial and CMS Launches AI Prior Authorization Pilot
New large randomized trial to test AI in mammography screening, while CMS launches a multi-state pilot using AI for Medicare prior authorizations.