Artificial intelligence decision support for breast ultrasound: potential to reduce benign biopsies.

Authors

Michael D C Fishman, Leslie R Lamb, Hana L Haver, Catherine S Giess, Pamela J DiPiro, Allyson L Chesebro, Stephanie H Chung, Eva C Gombos, Evguenia J Karimova, Leah H Portnow, Mansi A Saksena, Fatima A Salah, Gary X Wang, Manisha Bahl

Published in

Breast cancer research and treatment. Volume 220. Issue 1. Oct 20, 2026. Epub Oct 20, 2026.

Abstract

The purpose of this study is to evaluate the potential impact of a Food and Drug Administration (FDA)-cleared artificial intelligence (AI)-based decision support tool for breast ultrasound in reducing benign biopsies.
This study was conducted at two academic medical centers from March to May 2025 following the clinical implementation of an FDA-cleared AI-based decision support tool for breast ultrasound interpretation (Koios DS; New York, NY). Fourteen breast imaging radiologists documented BI-RADS 4 (suspicious) cases, along with the corresponding AI output. AI assessments were compared with biopsy pathology results to characterize AI classification within this selected cohort.
Overall, 271 lesions in 253 women (mean age, 54 years; range, 24-92 years) were categorized as BI-RADS 4 and were biopsied, aspirated, and/or surgically excised. Of these, 23.2% (63/271) yielded malignant pathology, while 76.8% (208/271) were benign. The AI tool classified 90.5% (57/63) of malignant lesions as BI-RADS 4 and 9.5% (6/63) as BI-RADS 2 or 3. Among benign cases, the AI tool assigned 41.8% (87/208) as BI-RADS 2 (benign), 18.8% (39/208) as BI-RADS 3 (probably benign), and 39.4% (82/208) as BI-RADS 4 (suspicious). Retrospective application of the AI output to biopsy triage decisions suggests the potential for reduction in benign biopsy recommendations, with 60.6% (126/208) of benign lesions classified by the AI tool as BI-RADS 2 or 3.
AI may help reduce benign biopsy recommendations among lesions assessed as BI-RADS 4 by radiologists; however, careful integration with radiologist judgment and clinical history is essential to ensure timely cancer diagnosis.

PMID:
42902009
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.