Psychiatry

Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis.

Traditional subjective measures are limited in the insight they provide into underlying behavioral d...

Neurofeedback for autism spectrum disorder: Current evidence, challenges, and future directions.

Neurofeedback therapy (NFT) has emerged as a promising noninvasive intervention for autism spectrum ...

Perception of vaccine myths in daily clinical practice: A survey of healthcare professionals.

Introduction. Misinformation is a barrier to immunization. The objective was to describe and categor...

Artificial intelligence in head and neck cancer rehabilitation services: current state and future perspectives.

PURPOSE OF REVIEW: This review evaluates the current state of artificial intelligence (AI) in head a...

In Situ Transmission Electron Microscopy Visualization of Electric-Field-Induced Phase Transitions at the Morphotropic Phase Boundary in Hf0.5Zr0.5O2.

Understanding electric-field-induced phase transitions is crucial for optimizing the ferroelectric a...

Looking back to the future of psychiatry: reflections on an international sojourn.

Psychiatry's metamorphosis continues to evolve over time as concepts of the brain's neuronal functio...

Predicting Adolescent Response to School-Based Mindfulness: A Secondary Analysis of the MYRIAD Trial.

IMPORTANCE: Depression most commonly first emerges during adolescence, making early prevention criti...

Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.

BACKGROUND: Somatic symptom disorder (SSD) is associated with an elevated risk of suicidality. Howev...

Prediction models for suicide reattempts by lasso regression through machine learning models: Single versus multiple suicide attempters.

The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also...

Differentiation between psychotic and non-psychotic major depression by the tabular prior-data fitted network.

BACKGROUND: Misdiagnosing psychotic major depression (PMD) as non-psychotic major depression (NPMD) ...

Predicting treatment response in psychosis using fMRI: A comprehensive review.

In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatmen...

Modeling the empathy-self-discovery paradox in Gen Z social behavior with NPD using artificial neural networks.

This study investigates the resurgence of the Myers-Briggs Type Indicator (MBTI) among Generation Z,...

Integrating qHTS and QSAR Models to Identify Safe GPCR-Targeted Compounds: A Focus on hERG-Dependent Cardiotoxicity.

G-protein-coupled receptors (GPCRs) are a diverse family of seven-transmembrane domain receptors tha...

Heterophily-Aware Spectral GCN for Population-Level Brain Disorder Prediction.

Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a p...

Threat discrimination of real-world social interactions in schizotypal traits.

Threat detection is compromised across the schizophrenia spectrum, often revealed by paranoia and de...

Peripheral blood transcriptomic biomarkers for predicting antidepressant response in major depressive disorder.

MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a subs...

Discriminating between major depressive disorder and bipolar depression: Aberrant EEG microstate dynamics and machine learning classification.

BACKGROUND: Major depressive disorder (MDD) and bipolar depression (BD) are common mood disorders wi...

Digital companionship: The interplay of conversational AI, loneliness, social anxiety, and quality of life among young adults.

This study examines the relationship between Conversational Artificial Intelligence (CAI) usage and ...

Predicting fine motor deficit in autism by measuring brain activities and characterizing motor impairments.

Motor impairments affect approximately 86.9% of children with Autism Spectrum Disorder (ASD), often ...

Psychotherapists versus artificial intelligence: let's not raise the white flag.