Introduction:
The integration of artificial intelligence (AI) in ophthalmology, specifically through the use of Optical Coherence Tomography (OCT) images, has marked a significant advancement in the detection and management of ocular diseases. The article compares the detection of eye conditions by health professionals using Optical Coherence Tomography (OCT) with AI abilities.

Review Methods:
Online databases were searched for articles discussing the effectiveness of AI in OCT analyses and assessment of the accuracy and agreement of AI algorithms with human experts. Key words included ‘OCT’, ‘AI’, ‘comparison’ and ‘effectiveness’’.

Results:
AI algorithms have demonstrated the capability to automatically segment retinal layers, detect and quantify pathological changes, and predict disease progression. The application of AI helps address the challenge of artifacts in OCT images, enhancing the accuracy of tissue structure segmentation and improving diagnostic precision.

Conclusions:
This article explores the comparative effectiveness of AI and human experts in diagnosing ocular conditions using OCT, highlighting AI’s potential to complement human expertise and improve patient outcomes. Despite the promising results, variability in AI performance across different studies underscores the need for more robust and standardized AI models, along with high-quality, diverse datasets to ensure consistent and generalizable results.