ArticleNature communications2023
Automatic correction of performance drift under acquisition shift in medical image classification.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
20 citing papers in PubMed.
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial.Nature medicine · 2026Article
- Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.Journal of imaging informatics in medicine · 2026Article
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI.Mayo Clinic proceedings. Digital health · 2026Review
- How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and Qualitative Analysis.Journal of imaging informatics in medicine · 2026Article
- Federated artificial intelligence monitoring service (FAMOS): an in silico feasibility study.BJR artificial intelligence · 2026Article
- Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation.Transactions on machine learning research · 2026Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Foundation models for radiology-the position of the AI for Health Imaging (AI4HI) network.Insights into imaging · 2025Review
- Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.Nature communications · 2025Article
- [The analysis of invention patents in the field of artificial intelligent medical devices].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Article
- Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.Nature medicine · 2025Observational
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- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology.Insights into imaging · 2024Article
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.
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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.