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.

翻译：近年来，预训练大型语言模型在自然语言处理领域取得了巨大成功。以往研究主要集中于通用及泛化领域，而针对医疗领域的专用大型语言模型研究相对较少。医疗领域对诊断的专业化与高精度要求，以及大规模数据收集的挑战，制约了大型语言模型在医疗场景中的应用与发展。在眼科领域，临床诊断主要依赖医生解读报告并做出诊断决策。为利用大型语言模型为医生提供决策支持，我们收集了三种模态的眼科报告数据，并对LLaMA2模型进行微调，成功构建了专用于眼科疾病诊断的大型语言模型"Ophtha-LLaMA2"。推理测试结果表明，即便使用较小的微调数据集，Ophtha-LLaMA2在眼科诊断中的表现仍显著优于其他大型语言模型。这证明Ophtha-LLaMA2在眼科疾病诊断中展现出令人满意的准确性与效率，能够作为眼科医生为患者提供更优诊断支持的有效工具。本研究为大型语言模型在眼科领域的应用提供了有益参考，同时展现了该领域的巨大潜力与前景。