The Role of Large Language Models in Ophthalmology: Opportunities, Risks, and the Critical Need for Safeguards
By
Shameer Mohamed Naleer
Safras Mohamed Naleer
September 17, 2026
Clinical Scorecard: The Role of Large Language Models in Ophthalmology: Opportunities, Risks, and the Critical Need for Safeguards
At a Glance
Category Detail
Condition Ophthalmology Key Mechanisms Large language models (LLMs) assist in clinical documentation, patient communication, and diagnostic triage. Target Population Patients in ophthalmology clinics, particularly those with conditions like diabetes and glaucoma. Care Setting Clinical settings utilizing AI tools for documentation and patient communication.
Key Highlights
LLMs can produce fluent patient communications but may reference incorrect clinical guidelines. LLMs do not integrate real-time clinical data, leading to potential inaccuracies. Hallucination in LLM outputs can result in clinically significant errors. Bias in training datasets may lead to less accurate outputs for under-represented patient groups. Current NHS guidance lacks specific direction for LLM use in ophthalmology. Guideline-Based Recommendations
Diagnosis
Management
LLM outputs should be validated against clinical standards before use. Monitoring & Follow-up
Performance data should be disaggregated by demographic subgroup. Risks
Clinicians must maintain accountability for LLM-generated content. Patient & Prescribing Data
Patients with diabetes and glaucoma, particularly in diverse demographic settings.
LLMs may not accurately reflect the needs of specific patient populations due to biases.
Clinical Best Practices
Qualified clinicians should review and countersign all patient-facing communications produced with LLM assistance. Clinical records should clearly identify AI-assisted outputs. Related Resources & Content
