ArticleKorean journal of radiology2022
Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.
Article in Korean journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- Artificial intelligence in mammography: a systematic review of the external validation.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2024Pooled it
- Clinical Performance Tradeoffs of ChatGPT-5.2 Thinking (OpenAI) Compared with Radiologist Interpretation in Biopsy-Referred Mammography: Cancer Detection, False Positives, and Laterality.Tomography (Ann Arbor, Mich.) · 2026Article
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis.Korean journal of radiology · 2025Article
- Review
- Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy.Life (Basel, Switzerland) · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.European journal of radiology open · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography.Journal of breast cancer · 2023Article
- Looking Back at 2022 and ahead to 2023 for theKorean journal of radiology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. MATERIALS AND
methodsA retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes.
resultsFifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (
conclusionAI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.
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