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增强眼科决策的多式推理剂:初步的现实世界的临床验证

Yijing Zhuang¹, Dong Fang¹, Pengfeng Li¹

¹Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, Guangdong, China.

Frontiers in cell and developmental biology

|August 7, 2025

概括

一种新的AI代理,ReasonAgent,在眼科诊断方面表现有前途,并且与GPT-4o和住院相比显著改善了治疗规划,特别是在罕见疾病中.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医疗决策支持医疗决策支持

背景情况:

目前的大型语言模型 (LLM) 由于领域知识差距和幻觉风险,难以进行专业眼科诊断.
精确的眼科护理需要整合成像,临床史和基于指南的知识,而单模式的LLM无法有效实现.
现有的人工智能系统缺乏与临床工作流程的协调,限制了它们在眼科中的实际应用.

研究的目的:

推出ReasonAgent,一个结构化的推理代理,旨在克服眼科决策中当前AI的局限性.
评估ReasonAgent的诊断和治疗规划能力与眼科住院医生和GPT-4o.
展示模块化人工智能架构的潜力,以提高临床决策的可靠性.

主要方法:

开发了ReasonAgent,集成多模式视觉分析,知识检索和诊断推理模块.
在30个现实眼科病例中验证了ReasonAgent,包括常见和罕见疾病.
使用统计分析,比较ReasonAgent在诊断和治疗规划方面的表现与GPT-4o和眼科住院医生.

主要成果:

ReasonAgent的诊断准确度与眼科住院医生的诊断准确度相当 (p=0.65).
在治疗规划方面,ReasonAgent显著优于GPT-4o (p=0.01) 和居民 (p<0.001),特别是在罕见疾病方面.
在极少数情况下,GPT-4o表现出漏洞,而ReasonAgent的混合设计通过结构化的推理来缓解错误.

结论:

ReasonAgent为临床眼科领域特定的AI提供了一个新的框架.
模块化AI架构可以通过与人类一致的推理途径来提高决策忠实性.
该研究强调了在复杂的临床环境中需要专门的AI解决方案,解决案例级异质性.

关键词:

在 GPT-4o 中.人工智能的人工智能是人工智能.大型语言模型.眼部疾病眼部疾病推理代理人的推理代理人

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