Artificial Intelligence for Diagnostic Guidance in Ocular Surface Disorders

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.