Ophtha-LLaMA2: A Large Language Model for Ophthalmology
Huan Zhao Qian Ling Yi Pan Tianyang Zhong Jin-Yu Hu Junjie Yao Fengqian Xiao Zhenxiang Xiao Yutong Zhang San-Hua Xu Shi-Nan Wu Min Kang Zihao Wu Zhengliang Liu Xi Jiang Tianming Liu Yi Shao
In recent years, pre-trained large language models (LLMs) have achieved
tremendous success in the field of Natural Language Processing (NLP). Prior
studies have primarily focused on general and generic domains, with relatively
less research on specialized LLMs in the medical field. The specialization and
high accuracy requirements for diagnosis in the medical field, as well as the
challenges in collecting large-scale data, have constrained the application and
development of LLMs in medical scenarios. In the field of ophthalmology,
clinical diagnosis mainly relies on doctors' interpretation of reports and
making diagnostic decisions. In order to take advantage of LLMs to provide
decision support for doctors, we collected three modalities of ophthalmic
report data and fine-tuned the LLaMA2 model, successfully constructing an LLM
termed the "Ophtha-LLaMA2" specifically tailored for ophthalmic disease
diagnosis. Inference test results show that even with a smaller fine-tuning
dataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis
compared to other LLMs. It demonstrates that the Ophtha-LLaMA2 exhibits
satisfying accuracy and efficiency in ophthalmic disease diagnosis, making it a
valuable tool for ophthalmologists to provide improved diagnostic support for
patients. This research provides a useful reference for the application of LLMs
in the field of ophthalmology, while showcasing the immense potential and
prospects in this domain.