Purpose of review The aim of this article is to assess the current state of teleophthalmology given the sudden surge in telemedicine demand in response to the novel coronavirus 2019 (COVID-19). Recent findings Recommendations and policies from government and national health organizations, combined with social distancing, have led to exponential increases in telemedicine use. Teleophthalmology can be integrated into ophthalmic care delivery. In the emergency room, teleophthalmology can be utilized to triage patients and diagnose common ophthalmic eye diseases. Ophthalmology practices can utilize real-time medicine to conduct many parts of an in-person exam. In cases where more complex diagnostic tools are warranted, a model incorporating telemedicine and focused in-person visits may still be beneficial. Innovative technologies emerging in the market allow for increased remote monitoring, screening, and management of adult and pediatric patients for common eye diseases. Summary COVID-19 created a demand for healthcare delivery that limits in-person examination and potential viral exposure. Teleophthalmology allows ophthalmologists to continue caring for patients while keeping physicians and patients safe. Although challenges still exist, the pandemic has accelerated the adoption of teleophthalmology. As a result, teleophthalmology will play an integral role in providing high-quality efficient care in the near future.

Alkali exposure dramatically changes the PC profile of cornea. Our data are consistent with penetration and hydrolysis as stochastic contributors to changes in PCs due to exposure to alkali for a finite duration and amount.

ImportanceLanguage-learning model–based artificial intelligence (AI) chatbots are growing in popularity and have significant implications for both patient education and academia. Drawbacks of using AI chatbots in generating scientific abstracts and reference lists, including inaccurate content coming from hallucinations (ie, AI-generated output that deviates from its training data), have not been fully explored.ObjectiveTo evaluate and compare the quality of ophthalmic scientific abstracts and references generated by earlier and updated versions of a popular AI chatbot.Design, Setting, and ParticipantsThis cross-sectional comparative study used 2 versions of an AI chatbot to generate scientific abstracts and 10 references for clinical research questions across 7 ophthalmology subspecialties. The abstracts were graded by 2 authors using modified DISCERN criteria and performance evaluation scores.Main Outcome and MeasuresScores for the chatbot-generated abstracts were compared using the t test. Abstracts were also evaluated by 2 AI output detectors. A hallucination rate for unverifiable references generated by the earlier and updated versions of the chatbot was calculated and compared.ResultsThe mean modified AI-DISCERN scores for the chatbot-generated abstracts were 35.9 and 38.1 (maximum of 50) for the earlier and updated versions, respectively (P = .30). Using the 2 AI output detectors, the mean fake scores (with a score of 100% meaning generated by AI) for the earlier and updated chatbot-generated abstracts were 65.4% and 10.8%, respectively (P = .01), for one detector and were 69.5% and 42.7% (P = .17) for the second detector. The mean hallucination rates for nonverifiable references generated by the earlier and updated versions were 33% and 29% (P = .74).Conclusions and RelevanceBoth versions of the chatbot generated average-quality abstracts. There was a high hallucination rate of generating fake references, and caution should be used when using these AI resources for health education or academic purposes.