The volume of CT exams being done in the world has been rising every year, which has led to radiologist burn-out. Large Language Models (LLMs) have the potential to reduce their burden, but their adoption in the clinic depends on radiologist trust, and easy evaluation of generated content. Presently, many automated methods are available to evaluate the reports generated for chest radiographs, but such an approach is not available for CT presently. In this paper, we propose a novel evaluation framework to judge the capabilities of vision-language LLMs in generating accurate summaries of CT-based abnormalities. CT slices containing an abnormality (e.g., lesion) were input to a vision-based LLM (GPT-4V, LLaVA-Med, and RadFM), and it generated a free-text summary of the predicted characteristics of the abnormality. Next, a GPT-4 model decomposed the summary into specific aspects (body part, location, type, and attributes), automatically evaluated the characteristics against the ground-truth, and generated a score for each aspect based on its clinical relevance and factual accuracy. These scores were then contrasted against those obtained from a clinician, and a high correlation ( 85%, p < .001) was observed. Although GPT-4V outperformed other models in our evaluation, it still requires overall improvement. Our evaluation method offers valuable insights into the specific areas that need the most enhancement, guiding future development in this field.

通过提出一种新颖的评估框架，判断视觉语言模型在生成基于CT的异常的准确摘要方面的能力，从而为辅助放射科医师减轻负担，并指导未来该领域的发展。

使用GPT-4进行自动评估的基于视觉LLM预测的分解