Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Exploring large language model for next generation of artificial intelligence in ophthalmology
26
Zitationen
5
Autoren
2023
Jahr
Abstract
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.
Ähnliche Arbeiten
Optical Coherence Tomography
1991 · 13.574 Zit.
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
2016 · 7.211 Zit.
Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040
2014 · 6.689 Zit.
YOLOv3: An Incremental Improvement
2018 · 5.881 Zit.
Ranibizumab for Neovascular Age-Related Macular Degeneration
2006 · 5.799 Zit.