The involvement of clinicians in the most highly cited publications on artificial intelligence in ophthalmology indexed journals

Purpose: Significant advances in artificial intelligence (AI) have led to promising applications in ophthalmology. This study highlights the involvement of clinicians in the most cited ophthalmology publications on AI in ophthalmology journals indexed by Web of Science. Methods: Articles examining AI in ophthalmology journals were processed from Web of Science. After selecting relevant articles, we performed bibliometric analyses at the article and author levels as of March 2024. The primary outcome measure was the number of citations per article. Secondary outcomes included article measures (publication year, subspecialties, article type, databases, imaging) and author attributes (gender, academic metrics, location). Results: The top 100 publications were cited between 58 and 734 times, with a median of 91 citations. Publication reprint addresses were mainly based in America (44) and in Europe (22). Common subspecialties were retina (60), glaucoma (44) and cornea (18). Most imaging modalities were fundus photography (47), optical coherence tomography (47) and visual fields (19). 76 studies were aimed at the development and evaluation of a diagnostic technology. Some private databases (44 %) and public databases (40 %) were specified. Among the 399 men and 163 women authors, 297 were physicians (52.9 %). Women and men had significantly different h-indexes (women: 23 [interquartile range (IQR): 13–46] vs. men: 38.5 [17–65]; P = 0.02) and number of published documents (women: 104 [32–277] vs. men: 188.5 [63.5–394]; P = 0.03). Conclusion: The most influential articles in AI and ophthalmology by number of citations predominantly used AI for image recognition and improving diagnostic technology in retina followed by glaucoma. Physicians had a predominant role in these, highlighting the continued importance of clinician involvement in this research.