The application of large language models in ophthalmology
Journal Title: Eye Science (Yanke Xuebao) - Year 2025, Vol 40, Issue 3
Abstract
The application of Large Language Models (LLMs) in ophthalmology presents tremendous potential for the healthcare field, particularly in enhancing diagnostic efficiency, optimizing doctor-patient communication, and promoting personalized medicine. Through natural language processing technology, LLMs can assist doctors in summarizing and analyzing clinical data. They can integrate a patient's medical history, imaging data, and symptom descriptions to provide precise diagnostic support and reference for complex cases. LLMs can also help physicians quickly draft case reports,improving the management efficiency of medical records. In doctor-patient communication, LLMs can generate easy-to-understand explanations that help patients comprehend their conditions and treatment plans, thereby reducing communication barriers between doctors and patients. In telemedicine scenarios, LLMs can provide preliminary diagnostic suggestions by real-time analyzing images and textual information uploaded by patients, aiding doctors in remote diagnosis and treatment. Personalized medicine is another significant application direction for LLMs. By utilizing patients' genetic data and lifestyle habits, LLMs can assist physicians in formulating more precise personalized treatment plans and predicting postoperative recovery outcomes. Additionally, LLMs can self-optimize through continuous interaction with clinical data, enhancing their intelligence in ophthalmic diagnosis and treatment. Despite the broad application prospects of LLMs in the field of ophthalmology, challenges remain, including data privacy, model interpretability, and language understanding. In the future, LLMs will continue to serve as auxiliary tools for physicians,forming a new model of "human-machine collaboration" in diagnosis and treatment, ultimately providing better and more precise medical services to patients.
Authors and Affiliations
Wencheng ZHANG, Lingbing SHE, Jingjing HE
Keywords
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- EP ID EP767352
- DOI 10.12419/24100304
- Views 11
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