Opportunities and Challenges of Large Language Models in the Field of Ophthalmology

ABSTRACT

In recent years, ophthalmology, as an image-intensive discipline, has accumulated vast amounts of heterogeneous clinical data, including electronic medical records, imaging data, genomic information, and patient-reported outcomes. These datasets are characterized by multimodality, high dimensionality, and strong structural features, providing fertile ground for the application of large language models (LLMs). LLMs hold great potential in ophthalmology, with applications ranging from automated medical documentation and patient communication support to scientific literature summarization, intelligent diagnostic assistance, decision support systems, and personalized health education. Their capabilities in natural language understanding and cross-modal reasoning may significantly enhance both clinical and research efficiency in the field. However, the implementation of LLMs in ophthalmology faces multiple challenges, such as limited domain-specific corpora, concerns over data privacy, the risk of hallucinations and misinformation, insufficient cross-modal integration, and regulatory and ethical barriers to clinical deployment. Future progress will require interdisciplinary collaboration to optimize model performance, standardize data, and establish robust validation systems to ensure safe, explainable, and effective use in real-world scenarios. gave way to deep learning techniques that could capture