Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature

Comprehensive Summary

Peng et al. review the publication trends and key topics in artificial intelligence (AI) applications in ophthalmology over the past five years using bibliometric analysis methods. Peng el al. searched the Web of Science Core Collection (WoSCC) for articles on AI in ophthalmology, then used bibliometric software (CiteSpace, VOSviewer, and the R package Bibliometrix) to analyze publication trends and key topics. Peng el al. found that from 2020 to 2024 there were 21,725 documents published on AI in ophthalmology, with China and the U.S. being the top contributors. Peng el al. also found that most research focused on using deep learning and machine learning algorithms to diagnose retinal diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma. Overall, analysis showed that in ophthalmology, AI is mainly used to detect retinal diseases from images, but it can also help predict risks and manage conditions like farsightedness, astigmatism, and cataracts. However, AI still faces challenges with privacy, unclear decision-making, and errors from biased data, so using it safely in clinics is important.

Outcomes and Implications

This research is significant because it highlights the ways in which artificial intelligence (AI) is effectively used in the ophthalmology field and identifies the challenges of AI use in the field. From a clinical perspective, this research is relevant because it highlights the potential benefits and challenges of AI in medicine, helping physicians to better understand these challenges and improve the diagnosis and treatment of eye diseases.