Schizophrenia

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying di...

Development and validation of a personalised antipsychotic selection tool for first-line treatment in severe mental illness

Guidance is lacking on choice of first-line antipsychotic for individuals with incident severe mental illness (SMI). Patients may try several before an effective, well-tolerated drug is identified, delaying symptom improvement. We aimed to develop a personalised selection tool to identify the optimum first-line antipsychotic, based on individual sociodemographic and clinical characteristics. Risk ...

Deep Learning Cerebellar Magnetic Resonance Imaging Segmentation in Late-Onset GM2 Gangliosidosis: Implications for Phenotype

Late-onset Tay-Sachs (LOTS) disease and late-onset Sandhoff disease (LOSD) have long been considered indistinguishable due to similar clinical present...

Cross-Disorder Machine Learning Uncovers Schizophrenia Risk Variants Predictive of Alzheimer’s Disease

Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genet...

Silencer variants are key drivers of gene upregulation in Alzheimer’s disease

Alzheimer’s disease (AD), particularly late-onset AD, stands as the most prevalent neurodegenerative disorder globally. Owing to its substantial herit...

Development and validation of genomic biotypes for schizophrenia susceptibility from multiple polygenic scores

Understanding the genetic architecture of schizophrenia (SCZ) is invaluable for the development of personalized treatment. In three independent cohort...

Signal detection in the psychotic phenotype: Increased sensory precision and reduced decision threshold associated with psychotic-like experiences

Psychotic-like experiences may reflect disrupted signal detection, whereby individuals detect signals in noisy input that are unlikely to be present. ...

From Lectures to Learning Outcomes: Meaningful Integration of AI-Generated Content in Pre-Clerkship Medical Training

Large Language Models (LLMs) have shown considerable promise in knowledge processing and synthesis across various medical disciplines. In medical educ...

An interdisciplinary, randomized, single-blind evaluation of state-of-the-art large language models for their implications and risks in medical diagnosis and management

State-of-the-art (SOTA) large language models (LLMs) are poised to revolutionize clinical medicine by transforming diagnostic, therapeutic, and interd...

Lexical meaning is lower-dimensional in psychosis: the intrinsic geometry of the semantic space

Diverse language models (LMs), including large language models (LLMs) based on deep neural networks have come to provide an unprecedented opportunity ...

Can we detect the undetected? Comparing the prodromes of individuals with first episode psychosis detected and undetected by clinical high risk for psychosis services: an electronic health record study

The majority of first episode psychosis (FEP) patients are undetected (DET-) by clinical high risk for psychosis (CHR-P) services prior to onset and t...

MOKA: A pipeline for multi-omics bridged SNP-set kernel association test

The explosion of genomic and multi-omics data has created a need for scalable, reproducible tools that integrate functional annotations into genome-wi...

Zero-Shot Large Language Models for Long Clinical Text Summarization with Temporal Reasoning

Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajecto...

Large Language Models in Stroke Management: A Review of the Literature

Stroke care generates vast free-text records that slow chart review and hamper data reuse. Large language models (LLMs) have been trialed as a remedy ...

An ensemble multimodal approach for predicting first episode psychosis using structural MRI and cognitive assessments

Classification between first episode psychosis (FEP) patients and healthy controls is of particular interest to the study of schizophrenia. However, p...

Evaluating anti-LGBTQIA+ medical bias in large language models

Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While th...

Unveiling genetic architecture of white matter microstructure through unsupervised deep representation learning of fractional anisotropy maps

Fractional anisotropy (FA) derived from diffusion MRI is a widely used marker of white matter (WM) integrity. However, conventional FA-based genetic s...

Evaluating General-Purpose LLMs for Patient-Facing Use: Dermatology-Centered Systematic Review and Meta-Analysis

General-purpose large language models (LLMs) have rapidly evolved from experimental tools into widely adopted components of healthcare. Their prolifer...

Predicting Olanzapine Induced BMI increase using Machine Learning on population-based Electronic Health Records

Weight gain is a common side effect in patients treated with olanzapine (N05AH03), contributing to increased risks of metabolic complications such as ...

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...