ArticleRadiology. Artificial intelligence2022
Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics.
Article in Radiology. Artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.Frontiers in neuroscience · 2024Pooled it
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- Uncertainty quantification of U-Net based segmentation tool using conformal prediction.Journal of applied clinical medical physics · 2026Article
- MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma.Cancer research communications · 2026Article
- Predicting IDH Mutation in Glioma Patients Using Deep Learning Algorithms with Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- A modern radiologist's guide to artificial intelligence.Pediatric radiology · 2026Review
- ESR Essentials: common performance metrics in AI-practice recommendations by the European Society of Medical Imaging Informatics.European radiology · 2026Review
- Role of Model Size and Prompting Strategies in Extracting Labels from Free-Text Radiology Reports with Open-Source Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Potential of Image2Image translation in reducing AI bias attributed to differences in CT reconstruction methods: proof-of-concept study on a paired dataset.Frontiers in radiology · 2026Article
- A deep learning algorithm to detect proximal humerus fractures on radiographs.JSES reviews, reports, and techniques · 2025Article
- Article
- RadiomiX for Radiomics Analysis: Automated Approaches to Overcome Challenges in Replicability.Diagnostics (Basel, Switzerland) · 2025Article
- A Current Review of Generative AI in Medicine: Core Concepts, Applications, and Current Limitations.Current reviews in musculoskeletal medicine · 2025Review
- Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory - A Mini-review on the Benefits and Pitfalls of Its Implementation.Journal of clinical and translational pathology · 2025Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.Radiology. Imaging cancer · 2025Article
- Review
- MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs.Journal of medical imaging (Bellingham, Wash.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing use of machine learning (ML) algorithms in clinical settings raises concerns about bias in ML models. Bias can arise at any step of ML creation, including data handling, model development, and performance evaluation. Potential biases in the ML model can be minimized by implementing these steps correctly. This report focuses on performance evaluation and discusses model fitness, as well as a set of performance evaluation toolboxes: namely, performance metrics, performance interpretation maps, and uncertainty quantification. By discussing the strengths and limitations of each toolbox, our report highlights strategies and considerations to mitigate and detect biases during performance evaluations of radiology artificial intelligence models.
Indexed as
Identifiers
What OpenQuestion holds
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.