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.

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.