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OBJECTIVE: There has been much excitement on the use of large language models (LLMs) such as ChatGPT in ophthalmology. However, LLMs are limited in that they are trained on unverified information and do not cite their sources. This paper highlights a new methodology to create a generative AI chatbot to answer eye care related questions which uses only verified ophthalmology textbooks as data and cites its sources. SETTING: Yale School of Medicine Department of Ophthalmology and Visual Science. DESIGN/METHODS: Aeyeconsult, an ophthalmology chatbot, was developed using GPT-4 (the LLM used to power the publicly available chatbot ChatGPT-4), LangChain, and Pinecone. Ophthalmology textbooks were processed into embeddings and stored in Pinecone. User queries were similarly converted, compared to stored embeddings, and GPT-4 generated responses. The interface was adapted from public code. Both Aeyeconsult and ChatGPT-4 were tested on the same 260 questions from OphthoQuestions.com, with the first response from Aeyeconsult and ChatGPT-4 recorded as the answer. RESULTS: Aeyeconsult outperformed ChatGPT-4 on the OKAP dataset, with 83.4% correct answers compared to 69.2% (p = 0.0118). Aeyeconsult also had fewer instances of no answer and multiple answers. Both systems performed best in General Medicine, with Aeyeconsult achieving 96.2% accuracy. Aeyeconsult's weakest performance was in Clinical Optics at 68.1%, but it still outperformed ChatGPT-4 in this category (45.5%). CONCLUSION: LLMs may be useful in answering ophthalmology questions but their trustworthiness and accuracy is limited due to training on unverified internet data and lack of source citation. We used a new methodology, using verified ophthalmology textbooks as source material and providing citations, to mitigate these issues, resulting in a chatbot more accurate than ChatGPT-4 in answering OKAPs style questions.

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INTRODUCTION: This study characterizes retinal capillary blood flow in subjects with autosomal dominant Alzheimer's disease (ADAD)-causing mutations. METHODS: Carriers of PSEN1 or APP mutations were prospectively recruited and split into early-stage (ES) and late-stage (LS) groups. Controls were normal subjects and non-carriers from the at-risk group. Capillary blood flow was quantified using an optical coherence tomography angiography-based measure of erythrocyte flux through capillary segments. Statistical analyses were adjusted for correlation between two eyes of the same subject. RESULTS: ES carriers had significantly greater capillary blood flow than controls and LS carriers. ES and LS carriers had significantly greater capillary blood flow heterogeneity than controls. There was no difference between capillary blood flow of LS carriers and controls. DISCUSSION: ES ADAD carriers demonstrate increased retinal capillary blood flow and flow heterogeneity compared to controls. These findings support the hypothesis that increased perfusion is a pathophysiologic feature of presymptomatic stages of ADAD.

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Retinal imaging technology is rapidly advancing and can provide ever-increasing amounts of information about the structure, function and molecular composition of retinal tissue in humans in vivo. Most importantly, this information can be obtained rapidly, non-invasively and in many cases using Food and Drug Administration-approved devices that are commercially available. Technologies such as optical coherence tomography have dramatically changed our understanding of retinal disease and in many cases have significantly improved their clinical management. Since the retina is an extension of the brain and shares a common embryological origin with the central nervous system, there has also been intense interest in leveraging the expanding armamentarium of retinal imaging technology to understand, diagnose and monitor neurological diseases. This is particularly appealing because of the high spatial resolution, relatively low-cost and wide availability of retinal imaging modalities such as fundus photography or OCT compared to brain imaging modalities such as magnetic resonance imaging or positron emission tomography. The purpose of this article is to review and synthesize current research about retinal imaging in neurodegenerative disease by providing examples from the literature and elaborating on limitations, challenges and future directions. We begin by providing a general background of the most relevant retinal imaging modalities to ensure that the reader has a foundation on which to understand the clinical studies that are subsequently discussed. We then review the application and results of retinal imaging methodologies to several prevalent neurodegenerative diseases where extensive work has been done including sporadic late onset Alzheimer's Disease, Parkinson's Disease and Huntington's Disease. We also discuss Autosomal Dominant Alzheimer's Disease and cerebrovascular small vessel disease, where the application of retinal imaging holds promise but data is currently scarce. Although cerebrovascular disease is not generally considered a neurodegenerative process, it is both a confounder and contributor to neurodegenerative disease processes that requires more attention. Finally, we discuss ongoing efforts to overcome the limitations in the field and unmet clinical and scientific needs.

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