A Guide to Implementing LLMs in Practice
Retina specialists can use AI to streamline their communication, if they do it carefully. Here’s how.
Fatima Kalabi, MD; Mohammad Hossein Amirhosseini, BSc, MSc, PhD; and Rodrigo Anguita, MBBS, MD, FEBO, FICO
Retina Today 
KEY TAKEAWAYS Large language models are text-generation tools that can help retina specialists draft patient education and correspondences, teach cases, and research documents. Without careful prompting, AI models may overstate the likelihood of visual improvement, omit the need for ongoing treatment, or miss return precautions entirely. Safe adoption requires no patient-identifiable data, institution-approved platforms, careful prompting, and clinician review of every output. Large language models (LLMs) are not reading OCT scans for us yet, but they are changing how we talk to patients, draft correspondence, and prepare trainees for oral examinations. Understanding what they can and cannot do is now a practical clinical skill. For a broad overview of what LLMs are and their potential uses in the clinic, see AI Chatbots in the Retina Clinic. Here, we explore how, exactly, retina specialists can implement these tools safely in their workflow. DRAFTING PATIENT EDUCATION Retinal disease can present significant communication challenges. The difference between wet and dry AMD, the rationale for repeated intravitreal injections, or the urgency of a retinal detachment repair may be obvious to the specialist but bewildering to a newly diagnosed patient. LLMs can produce a first draft of a plain-language explanation, adjust reading level, or reformat content for a patient portal.1,2 In a cross-sectional study of ChatGPT-4 (OpenAI) responses to common questions about retinal detachment, macular hole, and epiretinal membrane surgery, retina specialists judged most answers to be appropriate.
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