Evaluating Progress in Automatic Chest X-Ray Radiology Report Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The application of AI to medical image interpretation tasks has largely been limited to the identification of a handful of individual pathologies. In contrast, the generation of complete narrative radiology reports more closely matches how radiologists communicate diagnostic information in clinical workflows. Recent progress in artificial intelligence (AI) on vision-language tasks has enabled the possibility of generating high-quality radiology reports from medical images. Automated metrics to evaluate the quality of generated reports attempt to capture overlap in the language or clinical entities between a machine-generated report and a radiologist-generated report. In this study, we quantitatively examine the correlation between automated metrics and the scoring of reports by radiologists. We analyze failure modes of the metrics, namely the types of information the metrics do not capture, to understand when to choose particular metrics and how to interpret metric scores. We propose a composite metric, called RadCliQ, that we find is able to rank the quality of reports similarly to radiologists and better than existing metrics. Lastly, we measure the performance of state-of-the-art report generation approaches using the investigated metrics. We expect that our work can guide both the evaluation and the development of report generation systems that can generate reports from medical images approaching the level of radiologists.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it