ArticlePatterns (New York, N.Y.)2023
Evaluating progress in automatic chest X-ray radiology report generation.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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
42 citing papers in PubMed.
- Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.European radiology · 2026Article
- Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning.Nature communications · 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
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- Multimodal Learning with Privileged Report Supervision for Generalizable Tuberculosis Detection on Chest Radiographs.Journal of medical systems · 2026Article
- Text-image alignment for ILD imaging: linking CXR evidence to CT quantification.NPJ digital medicine · 2026Article
- Hybrid framework for lesion-aware, clinically coherent chest X-ray report generation using contrastive learning and large language models.Scientific reports · 2026Article
- Swin-Qwen3: a three-stage vision-language framework for automated radiology report generation with multi-agent verification.Frontiers in radiology · 2026Article
- Bridging semantics and clinical fidelity: a section-based assessment of a vision-language model (RadVLM) for chest x-ray report generation.Frontiers in digital health · 2026Article
- Recent advances in artificial intelligence for radiology report generation: a brief review.BJR artificial intelligence · 2026Review
- Self-Reflective Chest X-Ray Report Generation with Clinical-Aware Detection and Multilevel Readability.Journal of medical systems · 2025Article
- EchoGraph system for automated quality assessment of echocardiography reports.NPJ digital medicine · 2025Article
- Artificial Intelligence-Driven Drafting of Chest X-Ray Reports: 2025 Position Statement From the Korean Society of Thoracic Radiology Based on an Expert Survey.Korean journal of radiology · 2025Article
- Scaling up biomedical vision-language models: Fine-tuning, instruction tuning, and multi-modal learning.Journal of biomedical informatics · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.Radiology · 2025Review
- Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data.Nature communications · 2025Article
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2025Article
- Advancements in Radiology Report Generation: A Comprehensive Analysis.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Artificial intelligence (AI) models for automatic generation of narrative radiology reports from images have the potential to enhance efficiency and reduce the workload of radiologists. However, evaluating the correctness of these reports requires metrics that can capture clinically pertinent differences. In this study, we investigate the alignment between automated metrics and radiologists' scoring of errors in report generation. We address the limitations of existing metrics by proposing new metrics, RadGraph F1 and RadCliQ, which demonstrate stronger correlation with radiologists' evaluations. In addition, we analyze the failure modes of the metrics to understand their limitations and provide guidance for metric selection and interpretation. This study establishes RadGraph F1 and RadCliQ as meaningful metrics for guiding future research in radiology report generation.
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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.