How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911923731169280 |
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| author | Zhu, Qingqing Hou, Benjamin Mathai, Tejas S. Mukherjee, Pritam Jin, Qiao Chen, Xiuying Wang, Zhizheng Cheng, Ruida Summers, Ronald M. Lu, Zhiyong |
| author_facet | Zhu, Qingqing Hou, Benjamin Mathai, Tejas S. Mukherjee, Pritam Jin, Qiao Chen, Xiuying Wang, Zhizheng Cheng, Ruida Summers, Ronald M. Lu, Zhiyong |
| contents | Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for evaluation. This study aims to bridge this gap by introducing a novel evaluation framework, named ``GPTRadScore''. This framework assesses the capabilities of multi-modal LLMs, such as GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, in generating descriptions for prospectively-identified findings. By employing a decomposition technique based on GPT-4, GPTRadScore compares these generated descriptions with gold-standard report sentences, analyzing their accuracy in terms of body part, location, and type of finding. Evaluations demonstrated a high correlation with clinician assessments and highlighted its potential over traditional metrics, such as BLEU, METEOR, and ROUGE. Furthermore, to contribute to future studies, we plan to release a benchmark dataset annotated by clinicians. Using GPTRadScore, we found that while GPT-4V and Gemini Pro Vision fare better, their performance revealed significant areas for improvement, primarily due to limitations in the dataset used for training these models. To demonstrate this potential, RadFM was fine-tuned and it resulted in significant accuracy improvements: location accuracy rose from 3.41\% to 12.8\%, body part accuracy from 29.12\% to 53\%, and type accuracy from 9.24\% to 30\%, thereby validating our hypothesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05680 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses Zhu, Qingqing Hou, Benjamin Mathai, Tejas S. Mukherjee, Pritam Jin, Qiao Chen, Xiuying Wang, Zhizheng Cheng, Ruida Summers, Ronald M. Lu, Zhiyong Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Automatically interpreting CT scans can ease the workload of radiologists. However, this is challenging mainly due to the scarcity of adequate datasets and reference standards for evaluation. This study aims to bridge this gap by introducing a novel evaluation framework, named ``GPTRadScore''. This framework assesses the capabilities of multi-modal LLMs, such as GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, in generating descriptions for prospectively-identified findings. By employing a decomposition technique based on GPT-4, GPTRadScore compares these generated descriptions with gold-standard report sentences, analyzing their accuracy in terms of body part, location, and type of finding. Evaluations demonstrated a high correlation with clinician assessments and highlighted its potential over traditional metrics, such as BLEU, METEOR, and ROUGE. Furthermore, to contribute to future studies, we plan to release a benchmark dataset annotated by clinicians. Using GPTRadScore, we found that while GPT-4V and Gemini Pro Vision fare better, their performance revealed significant areas for improvement, primarily due to limitations in the dataset used for training these models. To demonstrate this potential, RadFM was fine-tuned and it resulted in significant accuracy improvements: location accuracy rose from 3.41\% to 12.8\%, body part accuracy from 29.12\% to 53\%, and type accuracy from 9.24\% to 30\%, thereby validating our hypothesis. |
| title | How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses |
| topic | Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.05680 |