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使用基于文本的摘要区分眼睛表面瘤的ChatGPT的准确性:对pterygium,pseudopterygium和CIN进行比较分析

Laura De Luca¹, Matteo Mario Carlà², Feliciana Menna³

¹Department of Biomedical Sciences, Ophthalmology Clinic, University of Messina, Via Consolare Valeria, 98123, Messina, Italy. laura.deluca21@gmail.com.

International ophthalmology

|February 18, 2026

概括

人工智能 (AI) 在诊断眼睛表面病变方面表现有前途,如结膜内皮质瘤 (CIN),pterygium和 pseudopterygium. 聊天GPT-4实现了高准确度,有助于诊断和治疗建议,尽管它是高准确度.

科学领域:

眼科医生眼科眼科
人工智能在医学中的应用
眼科瘤学眼科瘤学

背景情况:

从诊断上来说,将结膜内皮质瘤 (CIN) 与和伪区分开来是具有挑战性的,特别是在非典型的呈现时.
人工智能 (AI),特别是大型语言模型 (LLM),通过合成复杂的临床数据,提供了作为决策支持工具的潜力.
准确的区分对于适当的管理和预防瘤病变的进展至关重要.

研究的目的:

为了评估ChatGPT-4在区分CIN,pterygium和pseudopterygium的诊断可靠性.
评估ChatGPT-4能够根据多式联络临床数据推适当的治疗策略的能力.
探索人工智能的实用性,作为眼皮表面疾病诊断中的决策支持工具.

主要方法:

60个经过病原学确认的病例概况 (CIN,pterygium,pseudopterygium) 被合成为标准化的临床简报.
标签包括人口统计细节,临床发现,AS-OCT和IVCM描述.
聊天GPT-4处理了这些文本摘要,并提供了诊断和治疗建议,与专家小组的共识相比较.

主要成果:

聊天GPT-4的诊断准确度为CIN的85%,的75%,伪的70%.
治疗建议与专家判断的一致性为80%,CIN的准确性最高 (90%).
错误分类最常见的是CIN和伪;该模型很少错过瘤诊断,但倾向于过度治疗良性病变.

结论:

通过使用多模式临床数据,ChatGPT-4在区分眼睛表面病变方面显示出显著的潜力.
人工智能工具可以作为一种有价值的分拣或决策支持系统,特别是在子专家访问有限的地方.
与基于图像的AI集成可以进一步提高这些具有挑战性的条件的诊断性能.

关键词:

人工智能辅助的诊断聊天GPT-4 聊天GPT-4 聊天结膜内皮层瘤症是什么大型语言模型.眼睛表面的新生体.伪菌 (Pseudopterygium) 是一种类型的植物.在Pterygium的基础上.

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