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Artificial intelligence applied to ophthalmology and optometry: A citation network analysis.

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This study analyzes artificial intelligence (AI) in ophthalmology and optometry publications. Key research areas identified include retinal pathology, anterior segment, and glaucoma, with deep learning for retinal disease diagnosis being highly cited.

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Area of Science:

  • Ophthalmology and Optometry
  • Artificial Intelligence (AI)

Background:

  • The integration of Artificial Intelligence (AI) into ophthalmology and optometry is rapidly expanding.
  • Understanding the research landscape and citation patterns is crucial for identifying emerging trends and influential work.

Purpose of the Study:

  • To analyze co-authorship and co-citation networks in AI for ophthalmology and optometry.
  • To identify key research areas and the most impactful publications in this field.

Main Methods:

  • A comprehensive literature search was conducted in the Web of Science database (1977-2021).
  • Search terms included "Artificial Intelligence" AND ("Ophthalmol*" OR "optometry").
  • Bibliometric analysis was performed using Citation Network Explorer, VOSviewer, and CiteSpace software.

Main Results:

  • A total of 1086 publications and 2348 citation networks were identified.
  • 2020 marked the peak publication year with 351 articles.
  • The most cited publication was "Clinically applicable deep learning for diagnosis and referral in retinal disease" (De Fauw et al., 2018).
  • Three main research clusters emerged: retinal pathology, anterior segment, and glaucoma.

Conclusions:

  • Citation network analysis provides valuable insights into the scientific landscape of AI in ophthalmology and optometry.
  • This study maps the evolution and key areas of AI research in eye care since 1977.