ArticleRadiology. Artificial intelligence2023
The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- LLaMA-XR: A Novel Framework for Radiology Report Generation Using LLaMA and QLoRA Fine Tuning.Bioengineering (Basel, Switzerland) · 2026Article
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- ESR Essentials: common performance metrics in AI-practice recommendations by the European Society of Medical Imaging Informatics.European radiology · 2026Review
- PulmoX-Net: a channel-attention enhanced deep learning model for multi-class pulmonary pathology classification in chest radiography.Frontiers in medicine · 2026Article
- Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data.Research square · 2025Article
- Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography.PLOS digital health · 2025Article
- Interpretability of AI race detection model in medical imaging with saliency methods.Computational and structural biotechnology journal · 2025Article
- Revolutionizing Radiology with Natural Language Processing and Chatbot Technologies: A Narrative Umbrella Review on Current Trends and Future Directions.Journal of clinical medicine · 2024Review
- Artificial intelligence: a primer for pediatric radiologists.Pediatric radiology · 2024Review
- Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review.ESMO real world data and digital oncology · 2024Review
- Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology.Insights into imaging · 2024Article
- Doctor's Orders-Why Radiologists Should Consider Adjusting Commercial Machine Learning Applications in Chest Radiography to Fit Their Specific Needs.Healthcare (Basel, Switzerland) · 2024Article
- Empirical data drift detection experiments on real-world medical imaging data.Nature communications · 2024Article
- Reconsidering Conclusions of Bias Assessment in Medical Imaging Foundation Models.Radiology. Artificial intelligence · 2023Article
- AI pitfalls and what not to do: mitigating bias in AI.The British journal of radiology · 2023Review
- The AI Generalization Gap: One Size Does Not Fit All.Radiology. Artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To externally test four chest radiograph classifiers on a large, diverse, real-world dataset with robust subgroup analysis. Materials and Methods: In this retrospective study, adult posteroanterior chest radiographs (January 2016-December 2020) and associated radiology reports from Trillium Health Partners in Ontario, Canada, were extracted and de-identified. An open-source natural language processing tool was locally validated and used to generate ground truth labels for the 197 540-image dataset based on the associated radiology report. Four classifiers generated predictions on each chest radiograph. Performance was evaluated using accuracy, positive predictive value, negative predictive value, sensitivity, specificity, F1 score, and Matthews correlation coefficient for the overall dataset and for patient, setting, and pathology subgroups. Results: Classifiers demonstrated 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity on the external testing dataset. Algorithms showed decreased sensitivity for solitary findings (43%-65%), patients younger than 40 years (27%-39%), and patients in the emergency department (38%-60%) and decreased specificity on normal chest radiographs with support devices (59%-85%). Differences in sex and ancestry represented movements along an algorithm's receiver operating characteristic curve. Conclusion: Performance of deep learning chest radiograph classifiers was subject to patient, setting, and pathology factors, demonstrating that subgroup analysis is necessary to inform implementation and monitor ongoing performance to ensure optimal quality, safety, and equity.
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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.