ArticleEuropean radiology2024
Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Methodological review of the level of statistical support declared in radiological research articles.The British journal of radiology · 2026Review
- Automatic lymphedema segmentation in T2-STIR MRI using an unsupervised clustering method.La Radiologia medica · 2026Article
- Explainable federated transformer framework for joint leukemia classification and stage prediction.Scientific reports · 2026Article
- Enhancing Document Classification Through Multimodal Image-Text Classification: Insights from Fine-Tuned CLIP and Multimodal Deep Fusion.Sensors (Basel, Switzerland) · 2025Article
- Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.JMIR medical informatics · 2024Article
- [Large language models from OpenAI, Google, Meta, X and Co. : The role of "closed" and "open" models in radiology].Radiologie (Heidelberg, Germany) · 2024Article
- The policies on the use of large language models in radiological journals are lacking: a meta-research study.Insights into imaging · 2024Article
- Development of image-based decision support systems utilizing information extracted from radiological free-text report databases with text-based transformers.European radiology · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo investigate the potential and limitations of utilizing transformer-based report annotation for on-site development of image-based diagnostic decision support systems (DDSS).
methodsThe study included 88,353 chest X-rays from 19,581 intensive care unit (ICU) patients. To label the presence of six typical findings in 17,041 images, the corresponding free-text reports of the attending radiologists were assessed by medical research assistants ("gold labels"). Automatically generated "silver" labels were extracted for all reports by transformer models trained on gold labels. To investigate the benefit of such silver labels, the image-based models were trained using three approaches: with gold labels only (M
resultsUtilizing transformer-based silver labels showed significantly higher macro-averaged AUC than training solely with gold labels (N = 1000: M
conclusionsTransformer-based annotation has potential for unlocking free-text report databases for the development of image-based DDSS. However, on-site development of image-based DDSS could benefit from more sophisticated annotation pipelines including further information than a single radiological report. CLINICAL RELEVANCE STATEMENT: Leveraging clinical databases for on-site development of artificial intelligence (AI)-based diagnostic decision support systems by text-based transformers could promote the application of AI in clinical practice by circumventing highly regulated data exchanges with third parties. KEY POINTS: • The amount of data from a database that can be used to develop AI-assisted diagnostic decision systems is often limited by the need for time-consuming identification of pathologies by radiologists. • The transformer-based structuring of free-text radiological reports shows potential to unlock corresponding image databases for on-site development of image-based diagnostic decision support systems. • However, the quality of image annotations generated solely on the content of a single radiology report may be limited by potential inaccuracies and incompleteness of this report.
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