Finding the Right Fit in Ophthalmology Residency

Bradford L. Tannen, MD, JD, MBA; Daniel B. Moore, MD; Jackson Laureano, MD; Gary Wörtz, MD; and Blake K. Williamson, MD, MPH

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Blake K. Williamson, MD, MPH, and Gary Wörtz, MD, speak with Bradford Tannen, MD, JD, MBA; Daniel B. Moore, MD; and Jackson Laureano, MD, about ophthalmology residency training at the University of Kentucky. The group weighs what matters beyond a strong application, including authentic research interests, away rotations, and the interpersonal qualities that make someone a good resident. They also discuss early surgical experience, how operative volume builds confidence, and why finding the right fit may matter more than reputation alone.

Harnessing Ocular Physiology: Fast and Sustained Tear Production With TRYPTYR® (acoltremon ophthalmic solution) 0.003% for Dry Eye Disease

Data from the pivotal COMET clinical trials reveal how the first topical ocular neuromodulator approved for the signs and symptoms of DED, TRYPTYR®, delivers a rapid increase in natural tear production.

John Hovanesian, MD (Moderator)*; Marjan Farid, MD*; Nate Lighthizer, OD*; Selina McGee, OD*

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Douglas Matsunaga, MD

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WHERE IT ALL BEGAN I was born and raised in Los Angeles and was always interested in science and biology. I attended the University of California Berkeley for undergraduate and studied molecular cell biology. While there, I involved myself in research for cancer and a gene therapy approach to cancer immunology. While I enjoyed the science and discovery of research, I had always been drawn to more direct patient care and went on to study at the University of Southern California (USC) Keck School of Medicine. MY PATH TO RETINA Shortly after starting medical school, my interest in ophthalmology was piqued by specialty lunch lectures and increased by elective specialty rotations. When looking into research opportunities, I was quickly drawn to the retina for its complex biology and the rapid expansion of retinal diagnostic technologies and treatments. My research on oxidative stress in retinal pigment epithelium cells and budding OCT angiography technology further cemented my retina interests. Upon starting my residency at Wills Eye Hospital, I should have approached all the specialties with an open mind, but I was fairly set on retina from the start. SUPPORT ALONG THE WAY I have been incredibly lucky to have been mentored by many people. The patient guidance and support of my medical school research leaders and counselors made my journey into ophthalmology not only possible but also brighter and more promising than I had thought possible.

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AI Chatbots in the Retina Clinic

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

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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.

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