Virtual Reality (VR) technology is widely recognized as a prominent technological paradigm. Its potential and promise in the domain of ophthalmology are substantial, and the evolution of VR technology has significantly influenced the contemporary landscape of ophthalmology. Numerous empirical studies have validated the practical utility of VR technology in domains such as ophthalmic disease treatment and surgery training. This paper offers a comprehensive overview of VR technology's utilization in ophthalmic disease treatment, student education, and surgery training, expands the application of VR technology in ophthalmic evaluation and disease diagnosis, discusses the challenges and limitations of VR technology in ophthalmology, and expounds on emerging trends and future developments of VR technology in ophthalmology. This endeavor aims to provide readers with an in-depth comprehension of the current status and future prospects of VR technology application in ophthalmology, with the ultimate objective of fostering more effective advancements and applications of VR technology in the realm of ophthalmology.

Virtual Reality (VR) technology is widely recognized as a prominent technological paradigm. Its potential and promise in the domain of ophthalmology are substantial, and the evolution of VR technology has significantly influenced the contemporary landscape of ophthalmology. Numerous empirical studies have validated the practical utility of VR technology in domains such as ophthalmic disease treatment and surgery training. This paper offers a comprehensive overview of VR technology's utilization in ophthalmic disease treatment, student education, and surgery training, expands the application of VR technology in ophthalmic evaluation and disease diagnosis, discusses the challenges and limitations of VR technology in ophthalmology, and expounds on emerging trends and future developments of VR technology in ophthalmology. This endeavor aims to provide readers with an in-depth comprehension of the current status and future prospects of VR technology application in ophthalmology, with the ultimate objective of fostering more effective advancements and applications of VR technology in the realm of ophthalmology.

Backgrounds: With the rapid development of artificial intelligence (AI), large language models (LLMs) have emerged as a potent tool for invigorating ophthalmology across clinical, educational, and research fields. Their accuracy and reliability have undergone tested. This bibliometric analysis aims to provide an overview of research on LLMs in ophthalmology from both thematic and geographical perspectives. Methods: All existing and highly cited LLM-related ophthalmology research papers published in English up to 24th April 2025 were sourced from Scopus, PubMed, and Web of Science. The characteristics of these publications, including publication output, authors, journals, countries, institutions, citations, and research domains, were analyzed using Biblioshiny and VOSviewer software. Results: A total of 277 articles from 1459 authors and 89 journals were included in this study. Although relevant publications began to appear in 2019, there was a significant increase starting from 2023. He M and Shi D are the most prolific authors, while Investigative Ophthalmology & Visual Science stands out as the most prominent journal. Most of the top-publishing countries are high-income economies, with the USA taking the lead, and the University of California is the leading institution. VOSviewer identified 5 clusters in the keyword co-occurrence analysis, indicating that current research focuses on the clinical applications of LLMs, particularly in diagnosis and patient education. Conclusions: While LLMs have demonstrated effectiveness in retaining knowledge, their accuracy in image-based diagnosis remains limited. Therefore, future research should investigate fine-tuning strategies and domain-specific adaptations to close this gap. Although research on the applications of LLMs in ophthalmology is still in its early stages, it holds significant potential for advancing the field.

Backgrounds: With the rapid development of artificial intelligence (AI), large language models (LLMs) have emerged as a potent tool for invigorating ophthalmology across clinical, educational, and research fields. Their accuracy and reliability have undergone tested. This bibliometric analysis aims to provide an overview of research on LLMs in ophthalmology from both thematic and geographical perspectives. Methods: All existing and highly cited LLM-related ophthalmology research papers published in English up to 24th April 2025 were sourced from Scopus, PubMed, and Web of Science. The characteristics of these publications, including publication output, authors, journals, countries, institutions, citations, and research domains, were analyzed using Biblioshiny and VOSviewer software. Results: A total of 277 articles from 1459 authors and 89 journals were included in this study. Although relevant publications began to appear in 2019, there was a significant increase starting from 2023. He M and Shi D are the most prolific authors, while Investigative Ophthalmology & Visual Science stands out as the most prominent journal. Most of the top-publishing countries are high-income economies, with the USA taking the lead, and the University of California is the leading institution. VOSviewer identified 5 clusters in the keyword co-occurrence analysis, indicating that current research focuses on the clinical applications of LLMs, particularly in diagnosis and patient education. Conclusions: While LLMs have demonstrated effectiveness in retaining knowledge, their accuracy in image-based diagnosis remains limited. Therefore, future research should investigate fine-tuning strategies and domain-specific adaptations to close this gap. Although research on the applications of LLMs in ophthalmology is still in its early stages, it holds significant potential for advancing the field.

Background: Research innovations inoculardisease screening, diagnosis, and management have been boosted by deep learning (DL) in the last decade. To assess historical research trends and current advances, we conducted an artifcial intelligence (AI)–human hybrid analysis of publications on DL in ophthalmology.

Methods: All DL-related articles in ophthalmology, which were published between 2012 and 2022 from Web of Science, were included. 500 high-impact articles annotated with key research information were used to fne-tune alarge language models (LLM) for reviewing medical literature and extracting information. After verifying the LLM's accuracy in extracting diseases and imaging modalities, we analyzed trend of DL in ophthalmology with 2 535 articles. 

Results: Researchers using LLM for literature analysis were 70% (p= 0.000 1) faster than those who did not, while achieving comparable accuracy (97% versus 98%, p = 0.768 1). The field of DL in ophthalmology has grown 116% annually, paralleling trends of the broader DL domain. The publications focused mainly on diabetic retinopathy (p = 0.000 3), glaucoma (p = 0.001 1), and age-related macular diseases (p = 0.000 1) using retinal fundus photographs (FP, p = 0.001 5) and optical coherence tomography (OCT, p = 0.000 1). DL studies utilizing multimodal images have been growing, with FP and OCT combined being the most frequent. Among the 500 high-impact articles, laboratory studies constituted the majority at 65.3%. Notably, a discernible decline in model accuracy was observed when categorizing by study design, notwithstanding its statistical insignificance. Furthermore, 43 publicly available ocular image datasets were summarized. 

Conclusion: This study has characterized the landscape of publications on DL in ophthalmology, by identifying the trends and breakthroughs among research topics and the fast-growing areas. This study provides an efcient framework for combined AI–human analysis to comprehensively assess the current status and future trends in the feld. 

Background: Research innovations inoculardisease screening, diagnosis, and management have been boosted by deep learning (DL) in the last decade. To assess historical research trends and current advances, we conducted an artifcial intelligence (AI)–human hybrid analysis of publications on DL in ophthalmology.

Methods: All DL-related articles in ophthalmology, which were published between 2012 and 2022 from Web of Science, were included. 500 high-impact articles annotated with key research information were used to fne-tune alarge language models (LLM) for reviewing medical literature and extracting information. After verifying the LLM's accuracy in extracting diseases and imaging modalities, we analyzed trend of DL in ophthalmology with 2 535 articles. 

Results: Researchers using LLM for literature analysis were 70% (p = 0.000 1) faster than those who did not, while achieving comparable accuracy (97% versus 98%, p = 0.768 1). The field of DL in ophthalmology has grown 116% annually, paralleling trends of the broader DL domain. The publications focused mainly on diabetic retinopathy (p = 0.000 3), glaucoma (p = 0.001 1), and age-related macular diseases (p = 0.000 1) using retinal fundus photographs (FP, p = 0.001 5) and optical coherence tomography (OCT, p = 0.000 1). DL studies utilizing multimodal images have been growing, with FP and OCT combined being the most frequent. Among the 500 high-impact articles, laboratory studies constituted the majority at 65.3%. Notably, a discernible decline in model accuracy was observed when categorizing by study design, notwithstanding its statistical insignificance. Furthermore, 43 publicly available ocular image datasets were summarized. 

Conclusion: This study has characterized the landscape of publications on DL in ophthalmology, by identifying the trends and breakthroughs among research topics and the fast-growing areas. This study provides an efcient framework for combined AI–human analysis to comprehensively assess the current status and future trends in the feld.