Application and Comparative Study of Large Language Model in Early Prediction of Glaucoma - Xu Manyi (ITM Web of Conferences)
Glaucoma is an important problem in global public health. However, the symptoms of glaucoma are not obvious in the early stages, and the correct diagnosis of glaucoma is very challenging. This paper aims to explore and compare the application effects of several advanced large language models in the early prediction of glaucoma. The study selected a variety of large language models, including Chat Generative Pre-trained transformer (ChatGPT), to build a glaucoma prediction model and analyze clinical data, genetic information, and lifestyle data of patients. The results of empirical studies show that ChatGPT and other models show high accuracy and good generalization ability in predicting the risk of glaucoma, especially in identifying high-risk patients. In addition, the performance of different models on specific data sets is different, suggesting that model selection should be optimized according to actual application scenarios and data characteristics. This study not only provides a new technical means for the early screening of glaucoma but also expands a new direction for the application of large language models in the medical field.