EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model

• URL: http://arxiv.org/abs/2504.13650v1
• Date: Fri, 18 Apr 2025 12:09:15 GMT
• Title: EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model
• Authors: Sijing Li, Tianwei Lin, Lingshuai Lin, Wenqiao Zhang, Jiang Liu, Xiaoda Yang, Juncheng Li, Yucheng He, Xiaohui Song, Jun Xiao, Yueting Zhuang, Beng Chin Ooi,
• Abstract summary: Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare.<n>Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data; (ii) Benchmark; and (iii) Model.<n>We propose the Eyecare Kit, which tackles the aforementioned three key challenges with the tailored dataset, benchmark and model.
• Score: 51.66031028717933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coarse-grained global visual understanding limits them in intelligent ophthalmic diagnosis. Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data. The lack of deeply annotated, high-quality, multi-modal ophthalmic visual instruction data; (ii) Benchmark. The absence of a comprehensive and systematic benchmark for evaluating diagnostic performance; (iii) Model. The difficulty of adapting holistic visual architectures to fine-grained, region-specific ophthalmic lesion identification. In this paper, we propose the Eyecare Kit, which systematically tackles the aforementioned three key challenges with the tailored dataset, benchmark and model: First, we construct a multi-agent data engine with real-life ophthalmology data to produce Eyecare-100K, a high-quality ophthalmic visual instruction dataset. Subsequently, we design Eyecare-Bench, a benchmark that comprehensively evaluates the overall performance of LVLMs on intelligent ophthalmic diagnosis tasks across multiple dimensions. Finally, we develop the EyecareGPT, optimized for fine-grained ophthalmic visual understanding thoroughly, which incorporates an adaptive resolution mechanism and a layer-wise dense connector. Extensive experimental results indicate that the EyecareGPT achieves state-of-the-art performance in a range of ophthalmic tasks, underscoring its significant potential for the advancement of open research in intelligent ophthalmic diagnosis. Our project is available at https://github.com/DCDmllm/EyecareGPT.

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