Background: Diabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis.Methods: The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database. The analysis included publication trends over time, citation patterns, institutional collaborations, and the emergence of keywords. Results: From 2014-2022, there was a steady increase in the number of publications, reaching a peak in 2021. India (26%), China (20.05%), and the USA (9.98%) were the major contributors to research output in this field. Among the publication venues, IEEE ACCESS stood out as the leading one, with 44 articles published. The research landscape has evolved from traditional image processing techniques to deep learning approaches. In recent years, there has been a growing emphasis on multimodal AI models. The analysis identified three distinct phases in the development of AI-based DR screening: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). Conclusions: The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies. Future directions suggest an increased focus on the integration of telemedicine, innovative AI algorithms, and real-world implementation of these technologies in real-world settings.

Background: Diabetic retinopathy (DR) screening using artificial intelligence (AI) has evolved significantly over the past decade. This study aimed to analyze research trends, developments, and patterns in AI-based fundus image DR screening from 2014 to 2024 through bibliometric analysis. Methods: The study used CiteSpace and Microsoft Excel to analyze 1,172 publications from the Web of Science Core Collection database. The analysis included publication trends over time, citation patterns, institutional collaborations, and the emergence of keywords. Results: From 2014-2022, there was a steady increase in the number of publications, reaching a peak in 2021. India (26%), China (20.05%), and the USA (9.98%) were the major contributors to research output in this field. Among the publication venues, IEEE ACCESS stood out as the leading one, with 44 articles published. The research landscape has evolved from traditional image processing techniques to deep learning approaches. In recent years, there has been a growing emphasis on multimodal AI models. The analysis identified three distinct phases in the development of AI-based DR screening: CNN-based systems (2014-2020), Vision Transformers and innovative learning paradigms (2020-2022), and large foundation models (2022-2024). Conclusions: The field has demonstrated a mature development in traditional AI approaches and is currently in the process of transitioning toward multimodal learning technologies. Future directions suggest an increased focus on the integration of telemedicine, innovative AI algorithms, and real-world implementation of these technologies in real-world settings.

Purpose: To explore the status of current global research, trends and hotspots in the field of lupus retinopathy (LR). Methods: Publications related to LR from 2003 to 2022 were extracted from the Web of Science Core Collection (WOSCC). Citespace 6.2.R4 software was used to analyze the raw data. Bibliometric parameters such as publication quality, countries, authors, international cooperation, and keywords were taken into account. Results: A total of 315 publications were retrieved. The annual research output has increased significantly since 2010, especially since 2017. Marmor MF, Lee BR, and Melles RB contributed the highest number of articles published on LR. The top three publishing countries were the USA, China, and UK. Stanford University, Hanyang University, and Harvard Medical School were the top three producing institutions in the world for LR research. The top ten commonly used keywords include the following: systemic lupus erythematosus, retinopathy, retinal toxicity, antimalarial, hydroxychloroquine, optical coherence tomography, antiphospholipid syndrome, microvascular, optic neuritis, optical coherence tomography angiography. The keywords "optical coherence tomography angiography" and "vessel density" have exploded in recent years. Conclusion: By analyzing the current body of LR literature, specific global trends and hotspots for LR research were identified, presenting valuable information to track cutting- edge progress and for future cooperation between various authors and institutions.

Purpose: To explore the status of current global research, trends and hotspots in the field of lupus retinopathy (LR). Methods: Publications related to LR from 2003 to 2022 were extracted from the Web of Science Core Collection (WOSCC). Citespace 6.2.R4 software was used to analyze the raw data. Bibliometric parameters such as publication quality, countries, authors, international cooperation, and keywords were taken into account. Results: A total of 315 publications were retrieved. The annual research output has increased significantly since 2010, especially since 2017. Marmor MF, Lee BR, and Melles RB contributed the highest number of articles published on LR. The top three publishing countries were the USA, China, and UK. Stanford University, Hanyang University, and Harvard Medical School were the top three producing institutions in the world for LR research. The top ten commonly used keywords include the following: systemic lupus erythematosus, retinopathy, retinal toxicity, antimalarial, hydroxychloroquine, optical coherence tomography, antiphospholipid syndrome, microvascular, optic neuritis, optical coherence tomography angiography. The keywords "optical coherence tomography angiography" and "vessel density" have exploded in recent years. Conclusion: By analyzing the current body of LR literature, specific global trends and hotspots for LR research were identified, presenting valuable information to track cutting- edge progress and for future cooperation between various authors and institutions.

Objective: This study aimed to analyze the research hotspots and frontiers in orthokeratology using bibliometric methods, providing a scientific and precise reference for both new and established researchers.Methods: A bibliometric analysis was conducted on literature related to orthokeratology over the past three decades within the Web of Science Core Collection (WoSCC).  Analytical tools available in the R software environment were employed, integrating a machine learning-based bibliometric approach.Results: A total of 740 articles concerning orthokeratology research were retrieved from the WoSCC. Research on orthokeratology has shown a consistent upward trend, with an annual growth rate of 18.75%. China, Australia, and the United States are the most prolific countries in this field, with China making the largest contribution. The journals with the highest number of publications are Optometry and Vision Science (n=110), Contact Lens and Anterior Eye (n=96), and Eye & Contact Lens (n=72. Meanwhile, Pauline Cho (n=76) and Cheung SW (n=47) are the most active authors. Over the past three decades, common keywords in research literature have highlighted key areas, including corneal reshaping in pediatric populations, the prevalence and progression of myopia, contact lenses, refractive errors, and changes in axial length.Conclusions: In summary, this bibliometric analysis presents a comprehensive overview of the current state of orthokeratology research. It aids in gaining a better understanding of how  this field has developed over the past 30 years.

Objective: This study aimed to analyze the research hotspots and frontiers in orthokeratology using bibliometric methods, providing a scientific and precise reference for both new and established researchers.Methods: A bibliometric analysis was conducted on literature related to orthokeratology over the past three decades within the Web of Science Core Collection (WoSCC).  Analytical tools available in the R software environment were employed, integrating a machine learning-based bibliometric approach.Results: A total of 740 articles concerning orthokeratology research were retrieved from the WoSCC. Research on orthokeratology has shown a consistent upward trend, with an annual growth rate of 18.75%. China, Australia, and the United States are the most prolific countries in this field, with China making the largest contribution. The journals with the highest number of publications are Optometry and Vision Science (n=110), Contact Lens and Anterior Eye (n=96), and Eye & Contact Lens (n=72. Meanwhile, Pauline Cho (n=76) and Cheung SW (n=47) are the most active authors. Over the past three decades, common keywords in research literature have highlighted key areas, including corneal reshaping in pediatric populations, the prevalence and progression of myopia, contact lenses, refractive errors, and changes in axial length.Conclusions: In summary, this bibliometric analysis presents a comprehensive overview of the current state of orthokeratology research. It aids in gaining a better understanding of how  this field has developed over the past 30 years.

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 1,459 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 1,459 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.