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

Wenhui Zhu; Xin Li; Xiwen Chen; Peijie Qiu; Vamsi Krishna Vasa; Xuanzhao Dong; Yanxi Chen; Natasha Lepore; Oana Dumitrascu; Yi Su; Yalin Wang

arXiv · 2503.03987

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

Wenhui Zhu·Xin Li·Xiwen Chen·Peijie Qiu·Vamsi Krishna Vasa·Xuanzhao Dong·Yanxi Chen·Natasha Lepore·Oana Dumitrascu·Yi Su·Yalin Wang

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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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BibTeX RIS

Wenhui Zhu, Xin Li, Xiwen Chen, Peijie Qiu, Vamsi Krishna Vasa, Xuanzhao Dong, Yanxi Chen, Natasha Lepore, Oana Dumitrascu, Yi Su, Yalin Wang. 2025-03-06. RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models. https://arxiv.org/abs/2503.03987

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