Schizophrenia

Machine Learning-Based Discrimination of Treatment-Resistant Schizophrenia Using Structural Brain Imaging: A Multi-Site Proof-of-Concept Study.

BACKGROUND: Treatment-resistant schizophrenia (TRS) affects 20-30% of individuals with schizophrenia, with persistent symptoms, functional impairment, and reduced quality of life. Clinical identification remains dependent on sequential antipsychotic trials despite reported structural brain differences between TRS and treatment-responsive schizophrenia (TxR). This study evaluated whether structural...

Dissecting multimodal predictors of psychosis risk and remission in youth at ultra‑high risk of developing psychosis: An exploratory study.

Early identification of individuals at risk of developing psychosis enables timely intervention and better clinical outcomes. Current approach relies on clinical assessments, such as the Comprehensive Assessment for At‑Risk Mental States (CAARMS), which provides an Ultra‑High‑Risk (UHR) classification but have limited predictive precision. Artificial intelligence (AI) models integrating neuroimagi...

Artificial intelligence agents and agentic artificial intelligence applied to precision medicine.

Precision medicine seeks to individualise care by integrating multimodal biomedical data, yet most deployed clinical artificial intelligence (AI) rema...

Metabolic biomarkers of treatment-resistant schizophrenia and cognitive correlates: insights from a machine learning.

Early identification of Treatment-resistant schizophrenia (TRS) remains a challenge. Metabolomics offers a promising strategy for identifying biomarke...

Optimization of hospitalization duration and cost structure for chronic schizophrenia patients in psychiatric hospitals.

OBJECTIVE: Chronic schizophrenia patients in psychiatric hospitals often have prolonged stays, high insurance resource consumption, and low efficiency...

Empowering Educators to Build AI Chatbots in Health Professions Education: Tutorial on a No-Code Design Workflow.

AI is entering clinical practice faster than health professions curricula can teach it, leaving many educators eager to use AI-based teaching tools bu...

Special article-EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee.

Artificial intelligence (AI) is rapidly advancing from automated measurement to full-report generation, yet existing frameworks do not provide a unifi...

Distinctive subcortical connectivity predicts treatment response in first-episode schizophrenia.

BACKGROUND: Subcortical regions are widely implicated in the pathological mechanisms and treatment of schizophrenia, and accumulating evidence, includ...

ChatGPT and Large Language Models in Contemporary Nursing.

OBJECTIVES: This narrative review synthesizes published evidence on the applications, benefits, limitations and governance considerations of ChatGPT a...

Machine learning-based integration of surface morphometric features identifies diagnostic and symptom-related cortical signatures of schizophrenia.

Schizophrenia (SCZ) is associated with widespread cortical abnormalities, however, neuroanatomical markers with robust diagnostic and clinical relevan...

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable mole...

Can We Improve the Prediction of Early Onset Mania and Hypomania in the Community?

BACKGROUND: There is broad agreement that the onset of bipolar disorders (BD) can be predicted by using combined estimates of familial, genetic and cl...

Multiancestry and Multitrait GWAS Meta-Analysis on Schizophrenia With a Sample of 322,321 Unveils Genetic Links to Chronic Lung Diseases.

Schizophrenia (SCZ) is a highly heritable psychiatric disorder, yet its genetic links with chronic pulmonary diseases remain poorly defined. Such link...

Large Language Models for Individualized Psychoeducational Tools for Psychosis: A Cross-Sectional Study.

OBJECTIVE: This study aimed to evaluate the quality of GPT-4-generated responses to commonly asked psychosis-related psychoeducational questions from ...

Knowledge-distilled diffusion models for improving cone-beam CT image quality with meta-learning under imbalanced data.

BACKGROUND: Adaptive radiation therapy (ART) relies on daily cone-beam CT (CBCT), yet its limited image quality hinders accurate dose calculation, par...

Retrieval-Augmented Generation in Radiology: A Scoping Review of Architectures, Imaging Applications, and Directions for Equitable Deployment.

Large language models (LLMs) are increasingly explored in radiology, yet concerns persist regarding hallucination and lack of factual grounding. Retri...

A primer on artificial intelligence for palliative care educators.

Artificial intelligence (AI) is rapidly being adopted in education in the health care professions, including in palliative care. Yet existing AI prime...

Do Large Language Models Support Nursing Care Planning? A Scoping Review of Applications, Evaluation Approaches and Outcomes.

AIM: To examine the overall performance of large language models (LLMs) in generating nursing care plans, clarify their role in nursing practice and i...

scReGAT: Leveraging Knowledge of Regulatory Interactions to Predict Long-range Gene Regulation at Single-cell Resolution.

Understanding gene regulation at single-cell resolution is crucial for unraveling development, disease, and cellular identity. We introduce single-cel...

Artificial Intelligence-Assisted Triage in Emergency Departments: A Scoping Review of Clinical Applications, Outcomes, and Implementation Challenges.

OBJECTIVE: Emergency department (ED) triage determines patient prioritization, early risk recognition, and allocation of limited resources. Artificial...