ArticleEuropean radiology2024
Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support-a reader study.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled 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
- The Influence of Large Language Model-Generated Artificial Intelligence Risk Estimates on Clinical Decision-Making in Breast Imaging: A Simulation-Based Study.Diagnostics (Basel, Switzerland) · 2026Article
- A novel statistical framework for quantifying risks and benefits of AI automation in screening mammography.PLOS digital health · 2026Article
- Influence of AI Decision Support on Radiologists' Performance and Visual Search in Screening Mammography.Radiology · 2025Article
- Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.European journal of nuclear medicine and molecular imaging · 2025Article
- PARP Pioneers: Using BRCA1/2 Mutation-targeted Inhibition to Revolutionize Breast Cancer Treatment.Current pharmaceutical design · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- [The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective].Radiologie (Heidelberg, Germany) · 2024Article
- Enhancing Radiologist Reading Performance by Ordering Screening Mammograms Based on Characteristics That Promote Visual Adaptation.Radiology · 2024Article
- The emperor has few clothes: a realistic appraisal of current AI in radiology.European radiology · 2024Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support. MATERIALS AND
methodThis reader study was based on selection of screening mammograms, including the original radiologist assessment, acquired in 2010 to 2013 at the Karolinska University Hospital, with a ratio of 1:1 cancer versus healthy based on a 2-year follow-up. A commercial AI system generated an exam-level positive or negative read, and image markers. Double-reading and consensus discussions were first performed without AI and later with AI, with a 6-week wash-out period in between. The chi-squared test was used to test for differences in contingency tables.
resultsMammograms of 758 women were included, half with cancer and half healthy. 52% were 40-55 years; 48% were 56-75 years. In the original non-enriched screening setting, the sensitivity was 61% (232/379) at specificity 98% (323/379). In the reader study, the sensitivity without and with AI was 81% (307/379) and 75% (284/379) respectively (p < 0.001). The specificity without and with AI was 67% (255/379) and 86% (326/379) respectively (p < 0.001). The tendency to change assessment from positive to negative based on erroneous AI information differed between readers and was affected by type and number of image signs of malignancy.
conclusionBreast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity. CLINICAL RELEVANCE STATEMENT: Radiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired. KEY POINTS: • Breast radiologists' sensitivity and specificity will be affected by changes brought by artificial intelligence. • Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity. • Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system.
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