Mapping AI in Ophthalmology: A Bibliometric Analysis of Machine Learning and Deep Learning

Abstract

Today, advanced technologies such as machine learning and deep learning demonstrate immense potential across various medical subdisciplines. Employing text mining and bibliometric analysis methods, this study aims to profoundly explore their specific applications within the field of ophthalmology. The findings indicate that within the literature examining artificial intelligence applications in ophthalmology, significant conceptual and methodological differences exist between deep learning and machine learning research. This divergence is particularly evident in the structural mapping of hot topics and the primary focus areas of contributing institutions. To some extent, these observed discrepancies highlight the necessity for further research to better understand the underlying mechanisms driving technological adoption in clinical settings. Ultimately, this comprehensive landscape provides both theoretical insights and practical references for future explorations in intelligent ophthalmic diagnostics.