Schizophrenia

Comparative Prediction of Psychotic and Mood Disorders with Multi-Model Machine Learning

Recently, there has been a surge in the number of mental health cases including paranoid schizophrenia (psychosis) and depression (mood disorder). This study conducted a comparative prediction of psychotic and mood disorders using multi-model machine learning (MLs), mainly: Logistic Regression (LR), Support Vector Classification (SVC), Random Forest (RF), and Extreme Gradient Boost (XGBoost). Meth...

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked population-based registry data from Catalonia, Spain, we developed machine learning-based prediction models for post-discharge intentional self-harm across different follow-up horizons, sex, and age groups, and evaluated their generalizability and robustness...

Multidimensional Evaluation of Large Language Models on the AAP In-Service Examination: Assessing Accuracy, Calibration, and Citation Reliability

Large language models (LLMs) have demonstrated rapid advancements in natural language understanding and generation, prompting their integration into b...

Leveraging fMRI non-stationarity for deep learning classifier training and feature detection to improve schizophrenia diagnosis

A neurobiologically-based diagnosis with superior reliability in place of clinical interview-based diagnosis is a primary goal in psychiatry. Dynamic ...

Does later chronotype cause poorer adolescent mental health? An Adolescent Brain Cognitive Development (ABCD) Study

This study investigated whether chronotype (biobehavioral preference for sleep and wake timing) across early adolescence impacts mental health symptom...

Probing the Surgical Competence of LLMs: A global health study leveraging AfriMedQA benchmarks

Global surgical care faces a severe workforce shortage, with more than 1.2 million additional specialists needed by 2030, particularly in low- and mid...

Regional brain aging patterns reveal disease-specific pathways of neurodegeneration

The heterogeneity of brain aging is a hallmark of neurological and psychiatric disorders, yet machine-learning tools used to characterize this process...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine and Risperidone in Alzheimer’s Disease: A Real□World Cohort Study with Treatment Effect Heterogeneity Analysis

Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer’s disease (AD), despite ongoing conce...

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to Wakefulness

To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...

Network-based Molecular Constraints on in vivo Synaptic Density Alterations in Schizophrenia

Converging neuroimaging, genetic, and post-mortem evidence highlights the fundamental role of synaptic density reductions in schizophrenia pathogenesi...

Medical Hallucination in Foundation Models and Their Impact on Healthcare

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accurac...

Information Leakage and Performance Overestimation in EEG-Based Schizophrenia Detection: Evidence from Literature and Empirical Analyses

Detecting schizophrenia (SZ) from electroencephalography (EEG) signals using machine- and deep learning models gained traction lately due to potential...

When AI Meets the FDA: An Evaluation of Large Language Models Performance in Regulatory and Clinical Trial Data Extraction, Synthesis, and Analysis

Clinical and population decision-making relies on the systematic evaluation of extensive regulatory evidence. The FDA drug reviews provide detailed in...

Large Language Models for Thematic Analysis in Healthcare Research: A Blinded Mixed-Methods Comparison with Human Analysts

Large language models (LLMs) are increasingly used for qualitative thematic analysis, yet evidence on their performance in analysing focus-group data,...

Evaluation of large language model chatbot responses to psychotic prompts

The large language model (LLM) chatbot product ChatGPT has accumulated 800 million weekly users since its 2022 launch. In 2025, several media outlets ...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

A medically grounded LLM agent–based tool to detect patient safety events in medical records

Large language models (LLMs) have shown incredible promise in medicine. While LLMs may be particularly useful in areas requiring extensive review of c...

TRUSTING: An International Multicenter Observational Study of Speech-Based Relapse Prediction in Psychosis Using Explainable AI

The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical pr...

IllusionBench: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Current Visual Language Models (VLMs) show impressive image understanding but struggle with visual illusions, especially in real-world scenarios. Ex...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...