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相关概念视频

Glaucoma: Overview01:25

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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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大型语言模型在青光眼中的应用:一个范围的综述.

Giovanni Rubegni1, Alessandra Cartocci2, Alessio Luschi2

  • 1Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.

Vision (Basel, Switzerland)
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概括

大型语言模型 (LLMs) 和视觉语言模型 (VLMs) 在格洛科马护理中显示出对患者教育和决策支持的希望. 然而,由于精度和集成的局限性,它们目前作为辅助工具的功能最好.

关键词:
聊天GPT 聊天 在GPT 聊天人工智能是一种人工智能.临床决策支持 临床决策支持玻璃眼 glaucoma 玻璃眼 玻璃眼 玻璃眼 玻璃眼大型语言模型.患者教育 患者教育视觉语言模型 视觉语言模型

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科学领域:

  • 眼科医生 眼科 眼科
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 大型语言模型 (LLM) 和视觉语言模型 (VLM) 在眼科中越来越多地用于患者教育,诊断和外科支持.
  • 它们在处理医疗信息方面的能力使得它们在青光眼治疗中非常有价值.

研究的目的:

  • 进行对绿眼中LLMs和VLMs的范围审查.
  • 总结它们的应用,性能和局限性,以告知未来的研究和临床使用.

主要方法:

  • 从2014年到2025年7月,在主要数据库 (PubMed,Scopus,Web of Science,arXiv,IEEE Xplore) 进行系统的文献搜索.
  • 纳入研究的LLMs/VLMs在青光眼的诊断,教育,或手术支持.
  • 根据PRISMA-ScR指南,由两个独立审查员进行选和审查.

主要成果:

  • 27项研究符合316个已识别的记录中的包含标准.
  • 应用被分为患者教育 (11项研究),诊断/风险预测 (10项研究) 和手术管理 (6项研究).

结论:

  • 目前,LLM最适合作为青光眼护理中的辅助工具,而不是自主系统.
  • 患者沟通和基于文本的决策支持的潜力很高,但准确性和多式联络整合需要改进.
  • 未来的工作应该专注于特定领域的LLM,增强的多式联络融合,患者可读性和道德框架.