Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities Qingyu Zhao1 ORCID logoand Kilian M. Pohl

https://arcr.niaaa.nih.gov/volume/46/1/leveraging-machine-learning-advance-alcohol-research-current-applications-challenges?utm_source=arcr-email&utm_medium=email&utm_campaign=pohl-2026-06&utm_content=arcr-listserv Alcohol use disorder is a complex condition with many underlying causes and consequences, making analysis of large sets of diverse data a priority in alcohol research. As described in a new Alcohol Research: Current Reviews article, the use of machine learning models may be helpful in predicting alcohol consumption, alcohol misuse, or treatment outcomes. The article discusses which kinds of data can be used to train machine learning models, what types of models have shown promise, and what outcomes are likely to yield the most accurate predictions. It also highlights existing challenges in applying machine learning to alcohol-related data sets, such as accounting for the numerous factors that may influence alcohol use and its outcome and potentially result in misleading data interpretation.