Synthesizing Risk Factors for Alcohol Use Disorder Using a Large Language Model
TL;DR
These findings demonstrate the value of an artificial intelligence-driven literature review for informing comprehensive strategies to ad- dress the multifactorial nature of AUD.
Abstract
Alcohol use disorder (AUD) remains a pervasive public health concern shaped by complex interactions among biological, psychological, and social determinants. Using large language models (LLMs), this study system- atically synthesizes key risk factors for AUD from fifty highly cited articles in the Web of Science Core Collection, published between 2021 and 2025. OpenAI’s GPT-4.1 was used to extract and rank determinants based on their association strength. Results highlight fourteen major domains, including hazardous drinking patterns, genetic predisposition, adverse childhood experiences, psychiatric comorbidities, and socioeco- nomic status, as consistently influential. Validation against expert manual review confirms high reliability for text-based synthesis, while accuracy is limited for interpreting tables and figures. These findings demonstrate the value of an artificial intelligence-driven literature review for informing comprehensive strategies to ad- dress the multifactorial nature of AUD.