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Updated: Mar 15, 2026

Author Spotlight: Anterior HR-OCT as a Non-Invasive Tool for Characterizing Ocular Surface Squamous Neoplasia
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Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders.

Amr Almobayed1, Omar Badla1, Pragat J Muthu1

  • 1Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.

Journal of Clinical Medicine
|March 14, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise in diagnosing ocular surface diseases (OSDs), matching or exceeding ophthalmologist accuracy. Future research should focus on large datasets and prospective trials for clinical reliability.

Keywords:
artificial intelligencecorneal ectasiadeep learningdry eye diseaseinfectious keratitisocular surface diseaseocular surface squamous neoplasiaocular surface tumorpigmented conjunctival lesionspterygium

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Diagnostic Imaging

Background:

  • Ocular surface diseases (OSDs) encompass a range of conditions, from dry eye disease (DED) to rare cancers like ocular surface squamous neoplasia (OSSN).
  • Artificial intelligence (AI) is emerging as a valuable tool for diagnosing various OSDs.

Purpose of the Study:

  • To review current AI applications in diagnosing major ocular surface pathologies.
  • To highlight AI's role in analyzing anterior segment imaging and other diagnostic measures for OSDs.

Main Methods:

  • Review of AI applications across OSD categories.
  • Examination of anterior segment imaging (slit-lamp, OCT, IVCM), meibography, tear film dynamics, and biochemical profiling.
  • Analysis of AI model performance compared to ophthalmologists.

Main Results:

  • AI models demonstrate performance comparable to or better than ophthalmologists in diagnosing OSDs.
  • AI offers consistent, reproducible, and accurate diagnostic guidance.
  • Current studies often lack external validation, prospective data, and standardized approaches, limiting generalizability.

Conclusions:

  • AI holds significant potential to improve diagnostic accuracy and accessibility for OSDs.
  • Future research must prioritize large, multicenter datasets, standardized frameworks, and prospective trials assessing human-AI collaboration.
  • Addressing current limitations will transition AI from experimental to clinically reliable tools.