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.

翻译：人工智能在医学影像领域展现出巨大潜力，但现有系统大多缺乏灵活性、可解释性和适应性——这些挑战在依赖多种成像模态的眼科领域尤为突出。本文提出EyeAgent，首个用于眼科全面且可解释的临床决策支持的智能体AI框架。该系统以大型语言模型（DeepSeek-V3）为核心推理引擎，能够解析用户查询，并动态协调涵盖23种成像模态的53个经过验证的眼科工具，以执行分类、分割、检测、图像/报告生成及定量分析等多样化任务。逐步消融分析显示，诊断准确率从仅使用5个通用工具的基线水平69.71%逐步提升至整合全部53个专用工具后的80.79%。在一项针对200个真实临床病例的专家评分研究中，EyeAgent实现了93.7%的工具选择准确率，并在准确性、完整性、安全性、推理能力和可解释性五个维度上获得超过88%的专家评分。在人机协作场景中，EyeAgent达到或超越了资深眼科医生的诊断水平；当作为辅助工具使用时，其将整体诊断准确率提升了18.51%，报告质量评分提高了19%，其中初级眼科医生获益最为显著。这些发现确立了EyeAgent作为一个可扩展且可信赖的眼科AI框架，并为构建模块化、多模态且与临床需求紧密结合的新一代AI系统提供了蓝图。