ArticleRadiology. Artificial intelligence2022
Mitigating Bias in Radiology Machine Learning: 2. Model Development.
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 43 papers.
What it found
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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
43 citing papers in PubMed.
- Uncertainty quantification of U-Net based segmentation tool using conformal prediction.Journal of applied clinical medical physics · 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
- Development and validation of claims-based algorithms to identify interstitial lung disease among Japanese patients with cancer in routine clinical practice using real-world data sources.Frontiers in oncology · 2026Article
- A deep learning algorithm to detect proximal humerus fractures on radiographs.JSES reviews, reports, and techniques · 2025Article
- AI Diffusion Models Generate Realistic Synthetic Dental Radiographs Using a Limited Dataset.Journal of imaging · 2025Article
- Artificial intelligence to automatically measure on radiographs the postoperative positions of the glenosphere and pivot point after reverse total shoulder arthroplasty.JSES international · 2025Article
- Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.Radiology · 2025Review
- 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
- Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.Radiology. Imaging cancer · 2025Article
- Review
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Bone Appetit: Skellytour Sets the Table for Robust Skeletal Segmentation.Radiology. Artificial intelligence · 2025Article
- MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.BMJ (Clinical research ed.) · 2025Article
- Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.Skeletal radiology · 2025Article
- Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.BJR open · 2025Review
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are increasing concerns about the bias and fairness of artificial intelligence (AI) models as they are put into clinical practice. Among the steps for implementing machine learning tools into clinical workflow, model development is an important stage where different types of biases can occur. This report focuses on four aspects of model development where such bias may arise: data augmentation, model and loss function, optimizers, and transfer learning. This report emphasizes appropriate considerations and practices that can mitigate biases in radiology AI studies.
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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.