{"ID":2842871,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09394","arxiv_id":"2511.09394","title":"EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology","abstract":"Artificial intelligence has shown promise in medical imaging, yet most existing systems lack flexibility, interpretability, and adaptability - challenges especially pronounced in ophthalmology, where diverse imaging modalities are essential. We present EyeAgent, the first agentic AI framework for comprehensive and interpretable clinical decision support in ophthalmology. Using a large language model (DeepSeek-V3) as its central reasoning engine, EyeAgent interprets user queries and dynamically orchestrates 53 validated ophthalmic tools across 23 imaging modalities for diverse tasks including classification, segmentation, detection, image/report generation, and quantitative analysis. Stepwise ablation analysis demonstrated a progressive improvement in diagnostic accuracy, rising from a baseline of 69.71% (using only 5 general tools) to 80.79% when the full suite of 53 specialized tools was integrated. In an expert rating study on 200 real-world clinical cases, EyeAgent achieved 93.7% tool selection accuracy and received expert ratings of more than 88% across accuracy, completeness, safety, reasoning, and interpretability. In human-AI collaboration, EyeAgent matched or exceeded the performance of senior ophthalmologists and, when used as an assistant, improved overall diagnostic accuracy by 18.51% and report quality scores by 19%, with the greatest benefit observed among junior ophthalmologists. These findings establish EyeAgent as a scalable and trustworthy AI framework for ophthalmology and provide a blueprint for modular, multimodal, and clinically aligned next-generation AI systems.","short_abstract":"Artificial intelligence has shown promise in medical imaging, yet most existing systems lack flexibility, interpretability, and adaptability - challenges especially pronounced in ophthalmology, where diverse imaging modalities are essential. We present EyeAgent, the first agentic AI framework for comprehensive and inte...","url_abs":"https://arxiv.org/abs/2511.09394","url_pdf":"https://arxiv.org/pdf/2511.09394v2","authors":"[\"Danli Shi\",\"Xiaolan Chen\",\"Bingjie Yan\",\"Weiyi Zhang\",\"Pusheng Xu\",\"Jiancheng Yang\",\"Ruoyu Chen\",\"Siyu Huang\",\"Bowen Liu\",\"Xinyuan Wu\",\"Meng Xie\",\"Ziyu Gao\",\"Yue Wu\",\"Senlin Lin\",\"Kai Jin\",\"Xia Gong\",\"Yih Chung Tham\",\"Xiujuan Zhang\",\"Li Dong\",\"Yuzhou Zhang\",\"Jason Yam\",\"Guangming Jin\",\"Xiaohu Ding\",\"Haidong Zou\",\"Yalin Zheng\",\"Zongyuan Ge\",\"Mingguang He\"]","published":"2025-11-12T15:03:29Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Language Model\"]","has_code":false}