Use of artificial intelligence to detect correlations between diseases

Abstract

The features of detecting correlations between diseases using artificial intelligence methods are considered. The possibilities of using machine learning algorithms to process large arrays of medical data and identify hidden dependencies between pathologies are analyzed. The importance of correlation analysis for early diagnosis, risk prediction, and personalization of treatment is substantiated. The main challenges of result interpretation are identified, particularly the distinction between correlation and causation.