Noncommunicable Eye Diseases Trend Related to Artificial Intelligence: A Bibliometric and Visualization Analysis

In recent years, artificial intelligence (AI) has transformed numerous sectors, including healthcare, and ophthalmology is no exception. The field has seen remarkable progress in using AI to detect, diagnose, and manage noncommunicable eye diseases (NCEDs), such as cataract, keratoconus, glaucoma, diabetic retinopathy, and age-related macular degeneration. This study presents a comprehensive bibliometric analysis of 4,280 articles between 2004 and 2026, revealing significant trends in AI-based NCED research. The literature search focused on a highly reputable database: Scopus. The selection of this database ensured a thorough exploration of the field, given its broad coverage of both technical and medical literature. The search strategy employed a carefully curated set of keywords to capture relevant articles and reviews. The field has experienced robust growth, with an average annual increase of 19.41% in publications, peaking in 2023 with 516 articles. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as the leading approach, surpassing traditional image processing techniques. Research in medical image analysis has primarily focused on age-related macular degeneration, glaucoma, and diabetic retinopathy, with an increasing emphasis on automated screening systems for early detection. Future trends may include a focus on explainable AI and attention mechanisms, integration with telemedicine, and development of more robust, generalizable models, highlighting its potential to revolutionize early diagnosis and management of eye diseases.

@article{Daud2026,
  title     = {Noncommunicable Eye Diseases Trend Related to Artificial Intelligence: A Bibliometric and Visualization Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Marizuana Mat Daud and W Mimi Diyana W Zaki and Laily Azyan Ramlan and Fazlina Mohd Ali and Jun Kit Chaw},
  doi       = {10.14569/IJACSA.2026.0170390},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170390}
}