Schizophrenia

Multimodal AI in Tissue Diagnostics: Vision-Language Models and the Future of Computational Pathology.

Vision-language models (VLMs) represent an emerging class of multimodal artificial intelligence (AI) systems that integrate visual information with natural-language understanding and generation. In computational pathology, VLMs provide a framework for aligning histologic morphology from whole slide images (WSIs) with pathology reports, and other text-based knowledge sources. This review summarizes...

Imaging features of affective and non-affective psychosis.

Affective and non-affective psychoses exhibit heterogeneous clinical presentation, neurobiological underpinnings, and treatment response. This special issue integrates multimodal findings to examine overlapping and distinct neural substrates in the psychosis spectrum. In this context, longitudinal designs appear essential to observe the developmental trajectory of patients and advance real-world p...

Performance of large language models in data extraction for evidence synthesis: A systematic review.

OBJECTIVE: Data extraction is among the most resource-intensive and error-prone stages of systematic review production. Large language models (LLMs) o...

Artificial Intelligence and Generative Models in Hepatology: From Large Language Models to Digital Pathology in Liver Disease Diagnosis and Treatment.

Artificial intelligence (AI), particularly foundation and generative models, is reshaping the practice of hepatology through enhanced knowledge synthe...

Evaluating large language models for assessment of psychosis risk.

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P). The effectiveness of the CHR-P state is con...

Computational Analysis of Semantic and Sentiment Features in Natural Language for Schizophrenia.

BACKGROUND: Schizophrenia (SZ) patients exhibit abnormalities in language expression, including semantic confusion and apathy, which significantly imp...

A Locally Executable AI System for Improving Preoperative Patient Communication: Multidomain Clinical Evaluation.

BACKGROUND: Patients undergoing invasive procedures frequently experience anxiety and often have unanswered questions regarding the procedure. Althoug...

Decoding neuronal gene expression: integrative insights from omics and AI.

Neuronal functional diversity and pathological vulnerability are governed by multi-layered regulatory programs. While high-throughput omics and neuroi...

Statistical Competence and Editorial Policies in Biomedical Publications.

Misuse of statistical methods in biomedical research remains widespread, undermining scientific integrity and public health. Flawed analyses can lead ...

Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks.

We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classification using multimodal MRI, including sMRI, fMRI, and...

Integrating chemical priors and physical laws to mitigate hallucinations in structure-based drug design.

Generative artificial intelligence has shown great promise in structure-based drug design (SBDD), yet existing models often suffer from a fundamental ...

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This study examined how demographic, epilepsy-related, an...

Off-target effects of DREADD ligands revealed by an anesthesia emergence paradigm in mice.

Designer receptors exclusively activated by designer drugs (DREADDs) enable reversible control of specific neural circuits, but the pharmacological ne...

Using smartphone-based assessment of social experience to predict relapse in first-episode psychosis.

Social-affective changes are early indicators of psychosis relapse, yet their dynamic and subjective nature makes them difficult to capture between ro...

Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.

BACKGROUND: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large ...

Guideline-anchored retrieval-augmented generation outperforms baseline and literature-only configurations in gynecologic oncology decision support: A pre-integration benchmark.

INTRODUCTION: Large language models (LLMs) are being studied as oncology decision-support tools but can produce inaccurate outputs. We compared LLM pe...

Performance Gaps and Optimization Strategies in Chinese Medical Large Language Models Based on MedBench: Evaluation Study.

BACKGROUND: The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensur...

Real-time hallucination detection and intervention in medical LLMs via calibrated hidden-state probes.

OBJECTIVE: Most hallucination mitigation for large language models (LLMs) operates post-hoc, leaving safety-critical clinical deployment without real-...

Integrative multi-omics reveals MHC class II-mediated neuroinflammation and systemic metabolic dysregulation as transdiagnostic drivers in major brain disorders.

Psychiatric, neurodevelopmental, and neurodegenerative disorders, including Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD),...

Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Dementia affects over 55 million people worldwide. Mild cognitive impairment (MCI) often precedes Alzheimer's disease (AD). Clinical manag...