Depression

Suicide and self-harm in the age of generative artificial intelligence: The case of ChatGPT and rise of suicide-related lawsuits.

Self-harm and suicide trends have taken a new turn in the era of GenAI and communicative chatbots. Recent OpenAI's own report suggests that 1.2 million weekly ChatGPT users appear to be expressing suicidal thoughts, and 80,000 users are potentially experiencing mania and psychosis. At the same time, OpenAI and other AI companies are facing lawsuits in several countries alleging that their chatbots...

Explainable AI for public health surveillance: investigating the persistent crisis of intentional injury mortality (suicide and homicide) in the Americas.

Intentional injury mortality (IIM), comprising homicide and suicide, remains a critical public health crisis in the Americas, which not only has the highest regional homicide rates globally but is also the only region where suicide rates continue to rise. This study employs explainable artificial intelligence (XAI) to examine the structural and temporal drivers of IIM across 25 countries, based on...

Application and effectiveness of artificial intelligence in adolescent mental health support and therapy: a scoping review.

BACKGROUND: Adolescents are highly vulnerable to mental health conditions, yet traditional counseling models may not fully address their evolving need...

Curcumin alleviates depression-aggravated psoriasis by targeting MMP9-regulated apoptotic pathways.

Depression and psoriasis are highly comorbid, yet their shared molecular mechanisms remain poorly understood. Utilizing cross-sectional data from NHAN...

Age-related determinants of geriatric depression: an analysis of theory-driven and data-driven approaches.

BACKGROUND: Geriatric depression is a prevalent mental health condition whose risk profile may vary across age strata. This study examined whether dep...

Evaluation of suicide behavior screening tools using machine learning and variable importance measures.

Suicide screening in military primary care settings is often conducted with a small number of self-report questions. The PRImary care Screening Method...

Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.

BACKGROUND: Major depressive disorder (MDD) and treatment-resistant depression (TRD) are heterogeneous conditions in which key clinical details are sp...

Accuracy of Descriptive and Illustrative Capacity of Generative Artificial Intelligence in Aesthetic Surgery: A Study of Accelerated Facial Aging in Depressed Medical Residents.

BACKGROUND: With the integration of artificial intelligence (AI) into various medical specialties, its role in aesthetic surgery is rapidly gaining tr...

Integrating Anomaly Detection and LLM-Based Explanation Generation in Clinical Data Dashboards.

This paper presents an integrated approach that combines unsupervised anomaly detection with large language model (LLM)-based explanation generation t...

Challenges Faced by People with Depression Using AI Chatbots in Saudi Arabia.

Depression is a common mental health illness that affects hundreds of millions of people worldwide. In Saudi Arabia, depression is considered a major ...

PROSODIC: Predictive Intelligence for Secondary Prevention of Youth Suicide.

Suicide is currently the leading cause of preventable death among young people, yet early identification of reattempt risk remains limited by its mult...

Risk prediction of depression- and anxiety-related psychological distress in a large multistage survey sample of Chinese adults: a multi-algorithm ensemble machine-learning model with SHAP.

OBJECTIVE: To develop an interpretable ensemble machine-learning model to support risk stratification of elevated depression- and anxiety-related psyc...

An explainable machine learning framework for analyzing and predicting mental health problems among university students in Bangladesh.

Mental health problems (MHPs) among university students are an increasing public health concern globally, including in Bangladesh. While machine learn...

Experimental and predictive analysis of pile raft foundation in clayey soils using machine learning and MATLAB tools.

This study demonstrates the performance of piled raft foundations in a variety of clay soils collected from four states in southern India. Comprehensi...

A Foundation Model for Wearable Movement Data in Mental Health Research.

Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflectin...

The association of early life experiences with prenatal depression: A machine learning-based predictive model.

BACKGROUND: Prenatal depression is highly prevalent and associated with early-life adversities like adverse childhood experiences (ACEs) and school bu...

From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework.

The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study...

Urinary copper is linked to regional cortical volume reductions in adolescents with major depressive disorder.

Major depressive disorder (MDD) is highly prevalent among adolescents, but its neurobiological mechanisms remain unclear. Neuroimaging studies have sh...

An Electrical Capacitance Tomography Dataset for Image Reconstruction Benchmarking.

We introduce ECT-Bench, the first large-scale, experimentally acquired dataset for planar Electrical Capacitance Tomography (ECT) designed to evaluate...

The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.

Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primar...