Addictions

Rehab or not rehab: artificial intelligence helps humans identify rehabilitation Cochrane systematic reviews.

BACKGROUND: Rehabilitation is a core component of health systems worldwide, yet its conceptual boundaries remain heterogeneous and difficult to operationalize in research and evidence synthesis. Different classification systems have been proposed, including the pragmatic framework introduced by Levack et al. in 2019 and the more structured research-oriented definition developed by Cochrane Rehabil...

Recent advances and knowledge gaps in anticoagulation strategy with durable left ventricular assist devices.

Driven by technological advancements, donor shortages, and an aging population, utilization of left ventricular assist devices (LVAD), both as a bridge to transplant and as destination therapy, has risen in recent years for symptomatic patients with end-stage heart failure. With wide blood flow pathways, friction-free movement, and intrinsic pulsatility that reduce shear stress and promote more fa...

AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.

OBJECTIVES: To evaluate an artificial intelligence (AI) algorithm on three different mammography devices and assess screening performance using genera...

Evaluation of the physical and radiological properties of different Fricke gel formulations as water-equivalent materials and biological tissues.

Fricke gel dosimeters, also known as hydrogels, have emerged as attractive tools in clinical dosimetry due to the requirements of quality assurance pr...

Clinicians' and people who use drugs' perspectives on artificial intelligence in addiction medicine.

BACKGROUND AND AIMS: Artificial intelligence (AI) may improve addiction medicine workflows and health outcomes but raises questions about data privacy...

Proof of Concept of Large Language Models for Opioid Treatment Policy Surveillance: 97% Agreement With Subject Matter Experts.

OBJECTIVE: Policy surveillance typically involves detailed, time-consuming manual screening of policies for inclusion in a final dataset. This screeni...

Identifying risk factors for marijuana use among male high school students using machine learning: Implications for public health.

OBJECTIVES: To identify risk and protective factors associated with lifetime marijuana use among male high school students through an interpretable ma...

A Human-Governed Clinical Informatics Framework for Safe AI-Assisted Mental Health Counseling: Secondary Framework Development and Requirement Mapping Study.

BACKGROUND: Natural language processing and large language model systems are increasingly used to support mental health documentation, screening, and ...

Dynamical properties of ab initio water from machine-learning potentials.

We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models...

Evaluating cellular communication sensing for lapse risk prediction during early recovery from alcohol use disorder: A longitudinal observational study.

Alcohol Use Disorder (AUD) is a chronic, relapsing condition, and identifying periods of elevated lapse risk remains a major challenge in supporting r...

AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study.

BACKGROUND: Artificial intelligence (AI) can be beneficial in tailoring learning experiences to each student's learning style, needs, and preferences....

Development and validation of a machine learning-based model for identifying liver fibrosis in individuals with prior Schistosoma japonicum infection: a step toward precision management.

BACKGROUND: Liver fibrosis caused by Schistosoma japonicum may continue to progress even following successful praziquantel chemotherapy. Although live...

Interpretable machine learning for hikikomori screening: The adaptive HRI-15.

Hikikomori, or prolonged social withdrawal, is an issue of global relevance. The HRI-15 is a brief tool for its assessment. This study enhances its ut...

Artificial Intelligence in Addiction Scholarship: Transparency, Accountability, and Disclosure in Research and Publishing.

Artificial intelligence (AI) is embedded in addiction scholarship, not only as a methodological tool in research but also as a tool for manuscript pre...

Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.

Drug-induced cardiotoxicity, mainly driven by cardiac ion-channel blockade, remains a leading cause of drug attrition and post-market withdrawal, high...

Modeling and forecasting oyster norovirus outbreaks of long-range dependence on antecedent marine environmental conditions.

This study presents three artificial intelligence-based models - XGBoost, Random Forest (RF), and Deep Artificial Neural Network (DANN) - with 2-day l...

An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder.

Background: Alcohol Use Disorder is a heterogeneous condition where standard severity measures often fail to predict individual treatment responses. P...

Exploring patients' and public perspectives on an oral capsule for gastric submucosal delivery in Denmark and the USA: a mixed-methods study protocol.

INTRODUCTION: Oral capsules for gastric submucosal delivery represent a fundamentally new route of administration for biologics, which are currently l...

Withdrawal time, CADe and adenoma detection: a prospective study.

A catalyst-aware explainable machine learning framework for biodiesel yield prediction over metal-doped biochar and activated carbon catalysts.

Biodiesel production over metal-doped biochar and activated carbon (AC) catalysts involves complex nonlinear interactions among feedstock characterist...