Cataract is the leading cause of blindness and vision loss globally. The implementation of artificial intelligence (AI) in the healthcare industry has been on the rise in the past few decades and machine learning (ML) classifiers have shown to be able to diagnose patients with cataracts. A systematic review and meta-analysis were conducted to assess the diagnostic accuracy of these ML classifiers for cataracts currently published in the literature. Retrieved from nine articles, the pooled sensitivity was 94.8% and the specificity was 96.0% for adult cataracts. Additionally, an economic analysis was conducted to explore the cost-effectiveness of implementing ML to diagnostic eye camps in rural Nepal compared to traditional diagnostic eye camps. There was a total of 22,805 patients included in the decision tree, and the ML-based eye camp was able to identify 31 additional cases of cataracts, and 2546 additional cases of non-cataract
