ArticleJournal of the American Medical Informatics Association : JAMIA2016
Preparing a collection of radiology examinations for distribution and retrieval.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 5 of them syntheses that pooled it.
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Who cites it
171 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Large Language Model-Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic Review.JMIR medical informatics · 2026Pooled it
- Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.Sensors (Basel, Switzerland) · 2022Pooled it
- The reporting quality of natural language processing studies: systematic review of studies of radiology reports.BMC medical imaging · 2021Pooled it
- Cross-Domain Chest Radiograph Report Generation Under Dataset Shift: Bidirectional Audit Study.JMIR medical informatics · 2026Article
- BLIX-Ray: Chest X-Ray Report Generation with Vision-Language Models and Reinforcement Learning.Journal of imaging informatics in medicine · 2026Article
- An open vision-language model for diverse medical applications.Nature medicine · 2026Article
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- CONRep: Uncertainty-Aware Vision-Language Report Drafting Using Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- Patient-specific multimodal learning with multi-view contrastive alignment for chest X-ray report generation.Bioinformatics (Oxford, England) · 2026Article
- Retrieval-Augmented Generation in Radiology: A Scoping Review of Architectures, Imaging Applications, and Directions for Equitable Deployment.Journal of imaging informatics in medicine · 2026Review
- Autodidactic dense anatomical models.Medical image analysis · 2026Article
- A pretrained biomedical large language model for Persian biomedical text mining.Scientific reports · 2026Article
- Learning like a radiologist: a medical vision-language model for radiological image analysis via curriculum learning.NPJ digital medicine · 2026Article
- Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation.JMIR medical informatics · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- LLaMA-XR: A Novel Framework for Radiology Report Generation Using LLaMA and QLoRA Fine Tuning.Bioengineering (Basel, Switzerland) · 2026Article
- Article
111 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
objectiveClinical documents made available for secondary use play an increasingly important role in discovery of clinical knowledge, development of research methods, and education. An important step in facilitating secondary use of clinical document collections is easy access to descriptions and samples that represent the content of the collections. This paper presents an approach to developing a collection of radiology examinations, including both the images and radiologist narrative reports, and making them publicly available in a searchable database. MATERIALS AND
methodsThe authors collected 3996 radiology reports from the Indiana Network for Patient Care and 8121 associated images from the hospitals' picture archiving systems. The images and reports were de-identified automatically and then the automatic de-identification was manually verified. The authors coded the key findings of the reports and empirically assessed the benefits of manual coding on retrieval.
resultsThe automatic de-identification of the narrative was aggressive and achieved 100% precision at the cost of rendering a few findings uninterpretable. Automatic de-identification of images was not quite as perfect. Images for two of 3996 patients (0.05%) showed protected health information. Manual encoding of findings improved retrieval precision.
conclusionStringent de-identification methods can remove all identifiers from text radiology reports. DICOM de-identification of images does not remove all identifying information and needs special attention to images scanned from film. Adding manual coding to the radiologist narrative reports significantly improved relevancy of the retrieved clinical documents. The de-identified Indiana chest X-ray collection is available for searching and downloading from the National Library of Medicine (http://openi.nlm.nih.gov/).
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