ArticleNPJ digital medicine2025
Simulating mismatch between calibration and target population in AI for mammography the retrospective VAIB study.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Retrospective Validation of a Resource-Aware Assistive AI Tool for Screening Prostate Needle Biopsies and Classifying Prostate Adenocarcinoma into ISUP Grade Groups.Life (Basel, Switzerland) · 2026Article
- Subcategorisation of Data for AI Models in Healthcare: A Case Study in Mammography.Diagnostics (Basel, Switzerland) · 2026Article
- A National Audit of Mammography Systems Settings That May Affect the Output of Artificial Intelligence Software.Diagnostics (Basel, Switzerland) · 2026Article
- Over-detection and over-surveillance in breast screening: current status and the potential for artificial intelligence optimisation.Insights into imaging · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI cancer detection models require calibration to attain the desired balance between cancer detection rate (CDR) and false positive rate. In this study, we simulate the impact of six types of mismatches between the calibration population and the clinical target population, by creating purposefully non-representative datasets to calibrate AI for clinical settings. Mismatching the acquisition year between healthy and cancer-diagnosed screening participants led to a distortion in CDR between -3% to +19%. Mismatching age led to a distortion in CDR between -0.2% to +27%. Mismatching breast density distribution led to a distortion in CDR between +1% to 16%. Mismatching mammography vendors lead to a distortion in CDR between -32% to + 33%. Mismatches between calibration population and target clinical population lead to clinically important deviations. It is vital for safe clinical AI integration to ensure that important aspects of the calibration population are representative of the target population.
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Registered trials
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.