Artiaga et. al. conducted a scoping literature review regarding the utility of large language models (LLM’s) in ophthalmology. 46 articles were identified as relevant to both LLM’s and ophthalmology and classified into four main categories: clinical assistance, medical education, patient education, and research. The identified studies utilized many types of LLM’s, most commonly ChatGPT 3.5 and 4 and Bard (now Google Gemini). Studies varied greatly in terms of subspecialty and type of data used, but common trends of the potential benefits of LLM’s included determining diagnoses accurately, helping patients understand complex conditions, and making ophthalmic education more accessible. However, common drawbacks were that LLM’s need further training in order to be on par with expert accuracy before open access can be given to patients and students. Additional concerns include hallucinations, potential personal biases, and limited accuracy of LLM’s outside of English text. Security also remains a significant issue, which has been met with potential solutions such as chain of thought and fine tuning.