Exploring large language model for next generation of artificial intelligence in ophthalmology
In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs’ pote...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1291404/full |
| _version_ | 1797506246935314432 |
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| author | Kai Jin Lu Yuan Hongkang Wu Andrzej Grzybowski Juan Ye |
| author_facet | Kai Jin Lu Yuan Hongkang Wu Andrzej Grzybowski Juan Ye |
| author_sort | Kai Jin |
| collection | DOAJ |
| description | In recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs’ potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs’ competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs’ promising role in shaping AI’s future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices. |
| first_indexed | 2024-03-10T04:31:01Z |
| format | Article |
| id | doaj.art-9d572f1c3d5440248f56c79f5dffe549 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-10T04:31:01Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-9d572f1c3d5440248f56c79f5dffe5492023-11-23T05:11:09ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-11-011010.3389/fmed.2023.12914041291404Exploring large language model for next generation of artificial intelligence in ophthalmologyKai Jin0Lu Yuan1Hongkang Wu2Andrzej Grzybowski3Juan Ye4Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Ophthalmology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, ChinaEye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaInstitute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, PolandEye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaIn recent years, ophthalmology has advanced significantly, thanks to rapid progress in artificial intelligence (AI) technologies. Large language models (LLMs) like ChatGPT have emerged as powerful tools for natural language processing. This paper finally includes 108 studies, and explores LLMs’ potential in the next generation of AI in ophthalmology. The results encompass a diverse range of studies in the field of ophthalmology, highlighting the versatile applications of LLMs. Subfields encompass general ophthalmology, retinal diseases, anterior segment diseases, glaucoma, and ophthalmic plastics. Results show LLMs’ competence in generating informative and contextually relevant responses, potentially reducing diagnostic errors and improving patient outcomes. Overall, this study highlights LLMs’ promising role in shaping AI’s future in ophthalmology. By leveraging AI, ophthalmologists can access a wealth of information, enhance diagnostic accuracy, and provide better patient care. Despite challenges, continued AI advancements and ongoing research will pave the way for the next generation of AI-assisted ophthalmic practices.https://www.frontiersin.org/articles/10.3389/fmed.2023.1291404/fullartificial intelligencelarge language modelChatGPTophthalmologydiagnostic accuracy and efficacy |
| spellingShingle | Kai Jin Lu Yuan Hongkang Wu Andrzej Grzybowski Juan Ye Exploring large language model for next generation of artificial intelligence in ophthalmology Frontiers in Medicine artificial intelligence large language model ChatGPT ophthalmology diagnostic accuracy and efficacy |
| title | Exploring large language model for next generation of artificial intelligence in ophthalmology |
| title_full | Exploring large language model for next generation of artificial intelligence in ophthalmology |
| title_fullStr | Exploring large language model for next generation of artificial intelligence in ophthalmology |
| title_full_unstemmed | Exploring large language model for next generation of artificial intelligence in ophthalmology |
| title_short | Exploring large language model for next generation of artificial intelligence in ophthalmology |
| title_sort | exploring large language model for next generation of artificial intelligence in ophthalmology |
| topic | artificial intelligence large language model ChatGPT ophthalmology diagnostic accuracy and efficacy |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1291404/full |
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