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

Decomposing Vision-based LLM Predictions for Auto-Evaluation with GPT-4

We present a new system based on tracking the temporal evolution of stroke
lesions using an image registration technique on CT exams of the patient's
brain. The system is able to compare past CT exams with the most recent one
related to stroke event in order to evaluate past lesions which are not related
to stroke. Then, it can compare recent CT exams related to the current stroke
for assessing the evolution of the lesion over time. A new similarity measure
is also introduced for the comparison of the source and target images during
image registration. It will result in a cheaper, faster and more accessible
evaluation of the acute phase of the stroke overcoming the current limitations
of the proposed systems in the state-of-the-art.

基于图像配准技术的卒中病人 CT 扫描中的缺血区暂时评估，引入新的相似性指标，快捷、便捷、易于操作。