Diagnosis of cataracts traditionally involves a visual examination by a healthcare professional and various diagnostic tests, such as slit-lamp examination, tonometry, and pachymetry. These tests can be time-consuming and expensive, and in some parts of the world, may not be readily available.
However, recent advances in technology have shown that using machine learning algorithms can help diagnose cataracts more accurately, quickly, and inexpensively. A study published in the Journal of Ophthalmology reported that using a convolutional neural network for diagnosing cataracts yielded an accuracy of over 90%.

The use of machine learning for diagnosing cataracts involves the training of an algorithm using thousands of images of healthy and cataract-affected eyes. The algorithm learns to identify patterns and features that distinguish healthy eyes from those with cataracts.