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In recent years, pre-trained large language models (LLMs) have achievedtremendous success in the field of Natural Language Processing (NLP). Priorstudies have primarily focused on general and generic domains, with relativelyless research on specialized LLMs in the medical field. The specialization andhigh accuracy requirements for diagnosis in the medical field, as well as thechallenges in collecting large-scale data, have constrained the application anddevelopment of LLMs in medical scenarios. In the field of ophthalmology,clinical diagnosis mainly relies on doctors' interpretation of reports andmaking diagnostic decisions. In order to take advantage of LLMs to providedecision support for doctors, we collected three modalities of ophthalmicreport data and fine-tuned the LLaMA2 model, successfully constructing an LLMtermed the "Ophtha-LLaMA2" specifically tailored for ophthalmic diseasediagnosis. Inference test results show that even with a smaller fine-tuningdataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosiscompared to other LLMs. It demonstrates that the Ophtha-LLaMA2 exhibitssatisfying accuracy and efficiency in ophthalmic disease diagnosis, making it avaluable tool for ophthalmologists to provide improved diagnostic support forpatients. This research provides a useful reference for the application of LLMsin the field of ophthalmology, while showcasing the immense potential andprospects in this domain.

Ophtha-LLaMA2: A Large Language Model for Ophthalmology