RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models

• URL: http://arxiv.org/abs/2503.03987v1
• Date: Thu, 06 Mar 2025 00:19:54 GMT
• Title: RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
• Authors: Wenhui Zhu, Xin Li, Xiwen Chen, Peijie Qiu, Vamsi Krishna Vasa, Xuanzhao Dong, Yanxi Chen, Natasha Lepore, Oana Dumitrascu, Yi Su, Yalin Wang,
• Abstract summary: We introduce textitRetinalGPT, a multimodal conversational assistant for clinically preferred quantitative analysis of retinal images.<n>In particular, RetinalGPT outperforms MLLM in the generic domain by a large margin in the diagnosis of retinal diseases.
• Score: 17.579521693647383
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, Multimodal Large Language Models (MLLMs) have gained significant attention for their remarkable ability to process and analyze non-textual data, such as images, videos, and audio. Notably, several adaptations of general-domain MLLMs to the medical field have been explored, including LLaVA-Med. However, these medical adaptations remain insufficiently advanced in understanding and interpreting retinal images. In contrast, medical experts emphasize the importance of quantitative analyses for disease detection and interpretation. This underscores a gap between general-domain and medical-domain MLLMs: while general-domain MLLMs excel in broad applications, they lack the specialized knowledge necessary for precise diagnostic and interpretative tasks in the medical field. To address these challenges, we introduce \textit{RetinalGPT}, a multimodal conversational assistant for clinically preferred quantitative analysis of retinal images. Specifically, we achieve this by compiling a large retinal image dataset, developing a novel data pipeline, and employing customized visual instruction tuning to enhance both retinal analysis and enrich medical knowledge. In particular, RetinalGPT outperforms MLLM in the generic domain by a large margin in the diagnosis of retinal diseases in 8 benchmark retinal datasets. Beyond disease diagnosis, RetinalGPT features quantitative analyses and lesion localization, representing a pioneering step in leveraging LLMs for an interpretable and end-to-end clinical research framework. The code is available at https://github.com/Retinal-Research/RetinalGPT

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