Purpose: ChatGPT is an artificial intelligence language model, which uses natural language processing to simulate human conversation. It has seen a wide range of applications including healthcare education, research and clinical practice. This study evaluated the accuracy of ChatGPT in providing accurate and quality information to answer questions on myopia. Methods: A series of 11 questions (nine categories of general summary, cause, symptom, onset, prevention, complication, natural history, treatment and prognosis) were generated for this cross‐sectional study. Each question was entered five times into fresh ChatGPT sessions (free from influence of prior questions). The responses were evaluated by a five‐member team of optometry teaching and research staff. The evaluators individually rated the accuracy and quality of responses on a Likert scale, where a higher score indicated greater quality of information (1: very poor; 2: poor; 3: acceptable; 4: good; 5: very good). Median scores for each question were estimated and compared between evaluators. Agreement between the five evaluators and the reliability statistics of the questions were estimated. Results: Of the 11 questions on myopia, ChatGPT provided good quality information (median scores: 4.0) for 10 questions and acceptable responses (median scores: 3.0) for one question. Out of 275 responses in total, 66 (24%) were rated very good, 134 (49%) were rated good, whereas 60 (22%) were rated acceptable, 10 (3.6%) were rated poor and 5 (1.8%) were rated very poor. Cronbach's α of 0.807 indicated good level of agreement between test items. Evaluators' ratings demonstrated ‘slight agreement’ (Fleiss's κ , 0.005) with a significant difference in scoring among the evaluators (Kruskal–Wallis test, p < 0.001). Conclusion: Overall, ChatGPT generated good quality information to answer questions on myopia. Although ChatGPT shows great potential in rapidly providing information on myopia, the presence of inaccurate responses demonstrates that further evaluation and awareness concerning its limitations are crucial to avoid potential misinterpretation.

Myopia has reached epidemic levels in recent years. Stopping the development and progression of myopia is critical, as high myopia is a major cause of blindness worldwide. This overview aims at finding the association of time spent outdoors (TSO), near work (NW), and physical activity (PA) with the incidence, prevalence, and progression of myopia in children. Literature search was conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature, Cochrane Database of Systematic Reviews, ProQuest, and Web of Science databases. Systematic reviews (SR) and meta-analyses (MA) on the TSO, NW, and PA in relation to myopia were reviewed. Methodological nature of qualified studies were evaluated utilizing the Risk of Bias in Systematic Review tool. We identified four SRs out of which three had MA, which included 62 unique studies, involving >1,00,000 children. This overview found a protective trend toward TSO with a pooled odds ratio (OR) of 0.982 (95% confidence interval (CI) 0.979–0.985, I² = 93.5%, P < 0.001) per extra hour of TSO every week. A pooled OR 1.14 (95% CI 1.08–1.20) suggested NW to be related to risk of myopia. However, studies associating myopia with NW activities are not necessarily a causality as the effect of myopia might force children to indoor confinement with more NW and less TSO. PA presented no effect on myopia. Though the strength of evidence is less because of high heterogeneity and lack of clinical trials with clear definition, increased TSO and reduced NW are protective against myopia development among nonmyopes.

Purpose: With the introduction of ChatGPT, artificial intelligence (AI)‐based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human‐like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under‐reported. Recent Findings: Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question‐based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT‐4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI‐based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible‐sounding ‘fake’ responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI‐based LLMs. Summary: Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision‐making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.