A Clinically-Informed Framework for Evaluating Vision-Language Models in Radiology Report Generation: Taxonomy of Errors and Risk-Aware Metric
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Summary
This summary is machine-generated.This study introduces a new framework to evaluate AI-generated radiology reports, focusing on clinical risk and safety. It identifies common errors in vision-language models (VLMs) to improve diagnostic accuracy.
Area Of Science
- Artificial Intelligence
- Medical Imaging
- Natural Language Processing
Background
- Vision-language models (VLMs) show promise for automatic radiology report generation.
- Existing evaluation metrics for these reports are insufficient, lacking clinical specificity.
- There is a need for robust evaluation methods that consider patient safety and clinical relevance.
Purpose Of The Study
- To develop a clinically informed evaluation framework for VLM-generated radiology reports.
- To introduce a novel risk-aware metric for assessing the safety impact of AI-generated reports.
- To analyze the performance and identify vulnerabilities of current leading VLMs in radiology.
Main Methods
- Defined a taxonomy of 12 radiology-specific error types, annotated with clinical risk levels (low, medium, high) by physicians.
- Conducted a comprehensive error analysis of three VLMs (DeepSeek VL2, CXR-LLaVA, CheXagent) on 685 expert-annotated MIMIC-CXR cases.
- Introduced the Clinical Risk-weighted Error Score for Text-generation (CREST) metric to quantify safety impact.
Main Results
- Identified critical model vulnerabilities and common error patterns across evaluated VLMs.
- Revealed condition-specific risk profiles associated with different types of errors.
- Demonstrated the limitations of traditional NLP metrics in capturing clinically significant inaccuracies.
Conclusions
- The proposed framework provides a safety-centric foundation for evaluating medical report generation models.
- Findings offer actionable insights for developing and deploying more reliable AI tools in radiology.
- The CREST metric and error taxonomy can guide future improvements in VLM performance for clinical applications.
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