Evidence map›Paper›PMID 42902009›Full record

ArticleBreast cancer research and treatment2026

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

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 and 4 more

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Article in Breast cancer research and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Michael D C FishmanDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
Leslie R LambDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
Hana L HaverDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Catherine S GiessDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Pamela J DiPiroDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Allyson L ChesebroDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Stephanie H ChungDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Eva C GombosDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Evguenia J KarimovaDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Leah H PortnowDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Mansi A SaksenaDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
Fatima A SalahDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Gary X WangDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
Manisha BahlDepartment of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA. mbahl1@mgh.harvard.edu.ORCID https://orcid.org/0000-0002-7728-7690

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe 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.

methodsThis 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.

resultsOverall, 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.

conclusionAI 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.

Indexed as

Artificial IntelligenceBreastBreast NeoplasmsDecision Support Systems, ClinicalUltrasonography, MammaryAdultAgedAged, 80 and overBiopsyFemaleHumansMiddle AgedYoung AdultArtificial intelligenceBiopsyBreast cancerUltrasound

Identifiers

PMID42902009

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.