Evidence map›Paper›PMID 40839134›Full record

ArticleEuropean radiology2026

Combined use of two artificial intelligence-based algorithms for mammography triaging: a retrospective simulation study.

Hee Jeong Kim, Hak Hee Kim, Hye Joung Eom, Woo Jung Choi, Eun Young Chae, Hee Jung Shin, Joo Hee Cha

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hee Jeong KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Hak Hee KimDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. hhkim@amc.seoul.kr.ORCID http://orcid.org/0000-0001-9672-844X
Hye Joung EomDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Woo Jung ChoiDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Eun Young ChaeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Hee Jung ShinDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Joo Hee ChaDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate triaging scenarios involving two commercial AI algorithms to enhance mammography interpretation and reduce workload. MATERIALS AND

methodsA total of 3012 screening or diagnostic mammograms, including 213 cancer cases, were analyzed using two AI algorithms (AI-1, AI-2) and categorized as "high-risk" (top 10%), "minimal-risk" (bottom 20%), or "indeterminate" based on malignancy likelihood. Five triaging scenarios of combined AI use (Sensitive, Specific, Conservative, Sequential Modes A and B) determined whether cases would be autonomously recalled, classified as negative, or referred for radiologist interpretation. Sensitivity, specificity, number of mammograms requiring review, and abnormal interpretation rate (AIR) were compared against single AIs and manual reading using McNemar's test.

resultsSensitive Mode achieved 84% sensitivity, outperforming single AI (p = 0.03 [AI-1], 0.01 [AI-2]) and manual reading (p = 0.03), with an 18.3% reduction in mammograms requiring review (AIR, 23.3%). Specific Mode achieved 87.7% specificity, exceeding single AI (p < 0.001 [AI-1, AI-2]) and comparable to manual reading (p = 0.37), with a 41.7% reduction in mammograms requiring review (AIR, 17%). Conservative and Sequential Modes A and B achieved sensitivities of 82.2%, 80.8%, and 80.3%, respectively, comparable to single AI or manual reading (p > 0.05, all), with reductions of 9.8%, 49.8%, and 49.8% in mammograms requiring review (AIRs, 18.6%, 21.6%, 21.7%).

conclusionCombining two AI algorithms improved sensitivity or specificity in mammography interpretation while reducing mammograms requiring radiologist review in this cancer-enriched dataset from a tertiary center. Scenario selection should consider clinical needs and requires validation in a screening population. KEY POINTS: Question AI algorithms have the potential to improve workflow efficiency by triaging mammograms. Combining algorithms trained under different conditions may offer synergistic benefits. Findings The combined use of two commercial AI algorithms for triaging mammograms improved sensitivity or specificity, depending on the scenario, while also reducing mammograms requiring radiologist review. Clinical relevance Integrating two commercial AI algorithms could enhance mammography interpretation over using a single AI for triaging or manual reading.

Indexed as

AlgorithmsArtificial IntelligenceBreast NeoplasmsMammographyRadiographic Image Interpretation, Computer-AssistedTriageAgedComputer SimulationFemaleHumansMiddle AgedRetrospective StudiesSensitivity and SpecificityArtificial intelligenceBreastBreast neoplasmsMammography

Identifiers

PMID40839134

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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.