Breaking News From the AAO Annual Meeting in New Orleans (Part 1 of 2)

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Eyewire News the Podcast reports on new products and breaking news live from thee American Academy of Ophthalmology meeting in New Orleans. Learn more about your ad choices. Visit megaphone.fm/adchoices

Refocusing the Patient Experience—AI and Team-Based Care in Ophthalmology

How AI can increase efficiency within clinical workflows without replacing human connection.

Scott LaBorwit, MD

Cataract and Refractive Surgery Today

AI Chatbots in the Retina Clinic

Large language models are not coming for your job. But they may redefine what your job looks like.

Fares Antaki, MDCM

Retina Today

KEY TAKEAWAYS Large language models have embedded ophthalmic knowledge that is improving rapidly with each model generation. Practical applications for retina specialists span two categories: workflow improvement (electronic health record documentation, report generation) and clinical assistance (triage, decision support). Vibe coding allows retina specialists without programming experience to build custom clinical tools using large language models. AI has been in ophthalmology for years, and most retina specialists are familiar with AI as it relates to image analysis. Algorithms that detect diabetic retinopathy (DR) from fundus photographs or segment fluid on OCT have received the most attention and, in the case of autonomous DR screening, regulatory clearance.1,2 However, a second wave of AI is reaching the clinic, and this version can read, write, and reason with text. These large language models (LLMs) power the chatbots that are quietly changing how physicians work.3 WHAT ARE LARGE LANGUAGE MODELS? LLMs are a class of AI designed to understand and generate human language. The technology behind products such as ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic) is conceptually straightforward. During training, a model is shown massive amounts of text and learns to predict the next word in a sequence.4 Consider the phrase attributed to Heraclitus: “The only constant in life is change.”5 A model in training would see “The only constant in life is…” and learn to predict “change.” This process is repeated billions of times across the entirety of the internet, books, and encyclopedias. The result is a system with a broad representation of human knowledge.