Harefuah. 2024 Jan;163(1):37-42.
[Article in Hebrew]


Artificial intelligence (AI) was first introduced in 1956, and effectively represents the fourth industrial revolution in human history. Over time, this medium has evolved to be the preferred method of medical imagery interpretation. Today, the implementation of AI in the medical field as a whole, and the ophthalmological field in particular, is diverse and includes diagnose, follow-up and monitoring of the progression of ocular diseases. For example, AI algorithms can identify ectasia, and pre-clinical signs of keratoconus, using images and information computed from various corneal maps. Machine learning (ML) is a specific technique for implementing AI. It is defined as a series of automated methods that identify patterns and templates in data and leverage these to perform predictions on new data. This technology was first applied in the 1980s. Deep learning is an advanced form of ML inspired by and designed to imitate the human brain process, constructed of layers, each responsible for identifying patterns, thereby successfully modeling complex scenarios. The significant advantage of ML in medicine is in its' ability to monitor and follow patients with efficiency at a low cost. Deep learning is utilized to monitor ocular diseases such as diabetic retinopathy, age-related macular degeneration, glaucoma, cataract, and retinopathy of prematurity. These conditions, as well as others, require frequent follow-up in order to track changes over time. Though computer technology is important for identifying and grading various ocular diseases, it still necessitates additional clinical validation and does not entirely replace human diagnostic skill.

Publication types

  • English Abstract

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Glaucoma*
  • Humans
  • Infant, Newborn
  • Machine Learning
  • Ophthalmology*