Latest AI and machine learning research in schizophrenia for healthcare professionals.
Large Language Models (LLMs) exhibit a critical tendency to generate factually incorrect yet linguistically fluent outputs - a phenomenon termed hallucination - which poses serious risks in precision-critical applications. Existing mitigation strategies, including retrieval-augmented generation and self-consistency sampling, either introduce substantial inference latency or depend on external know...
The Active Thermochemical Tables (ATcT) methodology represents a paradigm shift from traditional sequential thermochemistry to a network-based framework in which all available experimental and theoretical determinations are simultaneously incorporated and statistically reconciled within an overdetermined thermochemical network (TN). This perspective examines the role of ATcT as critical data infra...
The human cortex is complex and heterogeneous, undergoing extensive expansion during development1,2. Our prior study of neurogenesis, including radial...
BACKGROUND: Large language models (LLMs) have shown promising performance on medical examinations across specialties. However, comparative evaluations...
Modern generative large language models (LLMs) are increasingly being evaluated in epilepsy-related clinical tasks, but the evidence remains fragmente...
BACKGROUND: Nonadherence to antipsychotics affects nearly half of patients with schizophrenia, leading to rehospitalization, suicidality, and reduced ...
Large language models (LLMs) and vision-language models represent a fundamentally different category of artificial intelligence (AI) compared to prior...
Neuropsychiatric symptoms (NPS) are the most clinically consequential manifestations of dementia, yet they are frequently underestimated as secondary ...
Chronic liver diseases are an increasing cause of morbidity and mortality in Latin America, driven by the convergence of alcohol and metabolic-associa...
BACKGROUND: Large language models (LLMs) are increasingly used by clinicians and learners for endodontic information, yet their reliability for irriga...
INTRODUCTION: The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect ...
BACKGROUND: Digital pathology supports whole-slide imaging, remote review, and computational analysis. Most pathology AI systems, however, remain rest...
Temporal information processing is critical for brain function, supporting neural computations such as novelty detection, adaptation, and temporal nor...
INTRODUCTION: Social and occupational impairment is common in early psychosis, yet predictors of functional outcome in real-world early intervention i...
IMPORTANCE: Large language models (LLMs) are increasingly used for scientific literature retrieval, yet their citation accuracy in specialized clinica...
AI-enabled self-management health tools are increasingly promoted within health care policy as part of digital self-management models for mental healt...
AIMS: Psychotic experiences (PEs) are relatively common in youth and are associated with increased risk for later psychotic disorders. Although inflam...
BACKGROUND AND HYPOTHESIS: Schizophrenia (SCZ) is characterized by deficits in emotional expression, with facial expressions serving as potential mark...
As molecular and cellular technologies have advanced, the need to analyze and interpret the resulting vast and multi-modal data into actionable intell...
Multimodal artificial intelligence (AI) is reshaping prostate cancer imaging by moving beyond MRI-only algorithms toward models that integrate multipa...