Your browser doesn't support javascript.
loading
Understanding natural language: Potential application of large language models to ophthalmology.
Yang, Zefeng; Wang, Deming; Zhou, Fengqi; Song, Diping; Zhang, Yinhang; Jiang, Jiaxuan; Kong, Kangjie; Liu, Xiaoyi; Qiao, Yu; Chang, Robert T; Han, Ying; Li, Fei; Tham, Clement C; Zhang, Xiulan.
Afiliación
  • Yang Z; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Wang D; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Zhou F; Ophthalmology, Mayo Clinic Health System, Eau Claire, Wisconsin, USA.
  • Song D; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Zhang Y; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Jiang J; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Kong K; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Liu X; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
  • Qiao Y; Shanghai Artificial Intelligence Laboratory, Shanghai, China.
  • Chang RT; Department of Ophthalmology, Byers Eye Institute at Stanford University, Palo Alto, CA, USA.
  • Han Y; Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, USA.
  • Li F; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China. Electronic address: lifei.aletheus@gmail.co
  • Tham CC; Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China; Hong Kong Eye Hospital, Kowloon, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Shatin, Hong Kong SAR, China. Electronic address: clemtham@cuh
  • Zhang X; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China. Electronic address: zhangxl2@mail.sysu.edu.
Asia Pac J Ophthalmol (Phila) ; 13(4): 100085, 2024.
Article en En | MEDLINE | ID: mdl-39059558
ABSTRACT
Large language models (LLMs), a natural language processing technology based on deep learning, are currently in the spotlight. These models closely mimic natural language comprehension and generation. Their evolution has undergone several waves of innovation similar to convolutional neural networks. The transformer architecture advancement in generative artificial intelligence marks a monumental leap beyond early-stage pattern recognition via supervised learning. With the expansion of parameters and training data (terabytes), LLMs unveil remarkable human interactivity, encompassing capabilities such as memory retention and comprehension. These advances make LLMs particularly well-suited for roles in healthcare communication between medical practitioners and patients. In this comprehensive review, we discuss the trajectory of LLMs and their potential implications for clinicians and patients. For clinicians, LLMs can be used for automated medical documentation, and given better inputs and extensive validation, LLMs may be able to autonomously diagnose and treat in the future. For patient care, LLMs can be used for triage suggestions, summarization of medical documents, explanation of a patient's condition, and customizing patient education materials tailored to their comprehension level. The limitations of LLMs and possible solutions for real-world use are also presented. Given the rapid advancements in this area, this review attempts to briefly cover many roles that LLMs may play in the ophthalmic space, with a focus on improving the quality of healthcare delivery.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oftalmología / Procesamiento de Lenguaje Natural Límite: Humans Idioma: En Revista: Asia Pac J Ophthalmol (Phila) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oftalmología / Procesamiento de Lenguaje Natural Límite: Humans Idioma: En Revista: Asia Pac J Ophthalmol (Phila) Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos