The article reviews the current literature on the application of large language models (LLMs) in ophthalmic care, highlighting their utility and potential future uses. LLMs, particularly ChatGPT, have gained popularity in generating patient information, clinical diagnoses, and passing ophthalmology examinations. Human experts demonstrated the highest proficiency (86%) in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%), and providing information and answering questions (84.6%). LLMs performed best in general ophthalmology but had reduced accuracy in subspecialties. Limitations include nonspecific and outdated training, inability to process images, lack of critical literature analysis, and ethical and copyright issues. The review emphasizes the need for comprehensive evaluations and the importance of human judgment in clinical decision-making. Future applications of LLMs in ophthalmic care include remote monitoring, evidence-based practice, image analysis, and gene discovery. However, caution is advised due to the limitations and potential risks associated with LLMs.The article reviews the current literature on the application of large language models (LLMs) in ophthalmic care, highlighting their utility and potential future uses. LLMs, particularly ChatGPT, have gained popularity in generating patient information, clinical diagnoses, and passing ophthalmology examinations. Human experts demonstrated the highest proficiency (86%) in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%), and providing information and answering questions (84.6%). LLMs performed best in general ophthalmology but had reduced accuracy in subspecialties. Limitations include nonspecific and outdated training, inability to process images, lack of critical literature analysis, and ethical and copyright issues. The review emphasizes the need for comprehensive evaluations and the importance of human judgment in clinical decision-making. Future applications of LLMs in ophthalmic care include remote monitoring, evidence-based practice, image analysis, and gene discovery. However, caution is advised due to the limitations and potential risks associated with LLMs.