The role of Artificial Intelligence in interventions to reduce alcohol consumption and smoking: A systematic review

Abstract Background: Prevalence of alcohol consumption and smoking is high across high-income countries, and interventions to decrease either can include behaviour change delivered digitally. Artificial intelligence (AI) is a broad field encompassing various techniques, which include algorithms that learn from data to perform automated tasks without explicit human programming, and could potentially increase the effectiveness of digital behaviour change interventions through personalisation and reducing barriers to engagement. This review summarises the evidence for the effectiveness of AI-assisted interventions to reduce alcohol consumption and smoking. Methods: We conducted a systematic review to identify and summarise evidence from randomised controlled trials (RCTs) of AI-assisted interventions for reducing alcohol consumption or smoking. Eligible trials were RCTs that reported results of an AI-assisted public health intervention for reducing engagement with alcohol consumption or smoking in a high-income country. We searched Medline (Ovid), Embase (Ovid), Web of Science (Core collection), and Scopus for relevant trials published between 2010 and 14 January 2025. We also searched for reviews of public health interventions for alcohol consumption or smoking published between 2023 and 14 January 2025, and extracted all references from relevant reviews for screening. We conducted forward and backward citation searching on all included trials (dates of searches: July to September 2025). Screening for trials was conducted independently by two reviewers. Two reviewers independently assessed risk of bias using the Cochrane Risk of Bias 2 tool. As the included trials were heterogeneous in terms of interventions, outcomes, and timepoints, we synthesised the results narratively. Results : We included 4 trials for reducing alcohol consumption (comprising 16 reports and 4,718 randomised participants): 3 trials used apps with chatbots and reported mixed evidence, and one trial, which was effective, used a rules-based AI that tailored website content. We included 12 trials for stopping smoking (comprising 31 reports and 68,659 randomised participants): 8 trials of apps or messenger chatbots and 4 trials of recommender systems, all of which had mixed evidence. For many trials, the interventions had multiple components, of which AI-assistance was only one, meaning the effectiveness of AI-assistance specifically could not be determined. Additionally, high risks of bias across almost all trials reduced confidence in the results. There were no trials using large language models (LLMs). Discussion : There is no strong evidence of a beneficial effect of AI-assistance in public health interventions for reducing alcohol consumption or smoking in high-income countries. Future research should better describe public health interventions that use AI and embed equity considerations into their design and analysis to ensure already disadvantaged groups are not harmed further by the adoption of AI-assisted interventions in public health.