Addictions

LLM-Guided Pain Management: Examining Socio-Demographic Gaps in Cancer vs non-Cancer cases

Large language models (LLMs) offer potential benefits in clinical care. However, concerns remain regarding socio-demographic biases embedded in their outputs. Opioid prescribing is one domain in which these biases can have serious implications, especially given the ongoing opioid epidemic and the need to balance effective pain management with addiction risk. We tested ten LLMs—both open-access and...

Integrating Multilevel, Multidomain and Multimodal Neuroimaging Factors to Predict Early Alcohol Exposure Trajectories Using Explainable AI

Alcohol consumption tends to increase from childhood to adolescence, and risk factors at the individual, family, and environmental level (multilevel, multidomain factors), as well as changes in brain structure and function, have been associated with the likelihood of developing alcohol use disorder (AUD) or binge drinking later in life. Most studies have focused on limited subsets of multilevel or...

AI Implementation in U.S. Healthcare and Its Association With Elder Mortality and Quality of Care

Hospitals are increasingly adopting artificial intelligence (AI) tools in clinical care. However, their overall impact on the health of older adults r...

Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model

The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, o...

SBDH-Reader: an LLM-powered method for extracting social and behavioral determinants of health from clinical notes

Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions....

Artificial Intelligence for Predicting Treatment Adherence in Opioid Use Disorder: A Scoping Review

Opioid use disorder (OUD) is a chronic condition in which an individual engages in the persistent use of opioids that causes significant distress and ...

Machine learning models to detect opioid misuse in Emergency Department patients at triage

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few r...

Effects of Parietal Cathodal tDCS during Game Cue Exposure on Internet Gaming Disorder: A Randomized Double-Blind Sham-Controlled Trial

Internet Gaming Disorder (IGD) is officially listed as a behavioral addiction, exhibits high prevalence and has inadequate treatment efficacy. Targeti...

Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care

Recent studies have found that stigmatizing terms can incline physicians to pursue punitive approaches to patient care. The intensive care unit (ICU) ...

Identifying Key Predictive Features for Opioid Use Disorder Using Machine Learning

Opioid Use Disorder (OUD) continues to pose a pressing public health challenge across the United States, highlighting the critical need for early and ...

Sex-Specific Diagnostic Subtypes in Adolescents Hospitalized for Substance Use Disorders Revealed by Transformer-Based Clustering

Substance use disorders (SUD) are a leading cause of psychiatric hospitalization among adolescents, yet the underlying diagnostic profiles and comorbi...

Key features associated with opioid misuse in chronic pain: A machine learning cross-sectional study

Opioid misuse remains a critical public health concern, associated with increased risk of overdose, psychiatric comorbidity, and societal costs. While...

Urinary pesticide profiles and liver disease risk in Thailand: a machine-learning risk-prediction model

Building on evidence linking urinary glyphosate to chronic liver disease (CLD) and hepatocellular carcinoma (HCC), we developed urinary pesticide prof...

AI-based synthetic simulation CT generation from diagnostic CT for simulation-free workflow of spinal palliative radiotherapy

Current radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particular...

Development of a Hypertension Risk Prediction Model using Nationally Representative Survey Data: A Machine Learning Approach and Web Application Deployment

Hypertension is a major modifiable risk factor for cardiovascular diseases. Early identification of high-risk individuals using predictive models can ...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can su...

Machine Learning Prediction of Pharmacogenetic Test Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: A Retrospective Cohort Study

Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due ...

Childhood Maltreatment and Risk for Illicit Substance Use: Evidence for Mid-Adolescence as a Sensitive Exposure Period

Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...

Oxytocin Enhances Social-Emotional Reciprocity in Autism

We evaluated whether oxytocin improves social-emotional reciprocity in children and adolescents with autism spectrum disorder (ASD) by conducting a se...

Clinical Implementation of an AI Algorithm for Substance Misuse Screening in Hospitalized Adults

Manual inpatient screening for substance misuse is labor-intensive and inconsistently applied. Evaluation of artificial intelligence (AI)–assisted scr...