Machine Learning Applications in Alcohol Research: A Systematic Review of Current Methods and Clinical Potential

This systematic review examines machine learning approaches applied to alcohol-related outcomes across 110 peer-reviewed studies published 2015–2025, focusing on predictions of alcohol consumption and alcohol use disorder diagnosis. The authors identify that most studies use single-modality data and conventional machine learning techniques, highlighting opportunities to advance personalized medicine for AUD through overcoming current methodological limitations.