Schizophrenia

Assessing the accuracy of 4 generative AI tools to guide dosing of long-acting injectable antipsychotics.

PURPOSE: The purpose of this study was to review the accuracy of 4 different artificial intelligence (AI) tools in providing dosing recommendations for initiation and missed doses of long-acting injectable antipsychotics (LAIAs). SUMMARY: The increasing number of LAIAs provides a valuable treatment option for individuals with schizophrenia and bipolar disorder who struggle with adherence. Currentl...

Harnessing Large Language Models in Neonatal IVH: Exploring RAG Methodology for Prognostic Variable Discovery.

OBJECTIVE: To evaluate whether large language models (LLMs) can autonomously synthesize existing literature and accurately extract prognostic variables for neonatal intraventricular hemorrhage (IVH) and its outcomes while assessing their capability for clinical feature ranking and risk stratification. STUDY DESIGN: This pilot study employed a systematic literature review combined with retrieval au...

Attitudes toward artificial intelligence among individuals with anxiety, depression, bipolar disorders, and schizophrenia.

BACKGROUND: Artificial intelligence (AI) increasingly supports medical diagnosis, interventions, and clinical decision-making. In various domains of h...

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.

Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts severe pressure on healthcare systems. Early detection...

Trust, truth and transparency: analysing the references underpinning AI-generated surgical information.

INTRODUCTION: Artificial intelligence (AI) chatbots, powered by large language models, are used increasingly for disseminating surgical information, b...

Anticipating incomplete patient-reported outcomes in schizophrenia: a machine learning approach to predict the occurrence of missing data.

Beyond artificial intelligence psychosis: a functional typology of large language model-associated psychotic phenomena.

A man breached Windsor Castle with a crossbow after his large language model (LLM)-based companion encouraged an assassination plan. A father's questi...

Biosignatures of cognitive basic symptoms mark a distinct neurodevelopmental pathway to schizophrenia.

Efforts to predict schizophrenia risk using biological data have been hampered by the heterogeneity of current "clinical-high-risk" (CHR-P) criteria, ...

Causal Discovery in Observational Medical Research: Scoping Review.

BACKGROUND: Observational data are fundamental to medical research but present formidable challenges for causal inference. Machine learning-based caus...

Classification of functional near-infrared spectroscopy (fNIRS) signals in schizophrenia and bipolar disorder using deep learning methods.

BACKGROUND: Schizophrenia (SCZ) and Bipolar Disorder (BD) are prevalent mental disorders. This study uses functional near-infrared spectroscopy (fNIRS...

EEG based detection of schizophrenia using asymmetry of entropy and CNN-LSTM model.

Schizophrenia is a severe neuropsychiatric disorder with a significant impact on individual's real-life functioning. It is characterized by abnormal a...

Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.

Artificial intelligence (AI) is increasingly used in mental health, yet its rehabilitation-oriented applications in schizophrenia have not been system...

From pharmacokinetic genes to polygenic scores and multi-omic integration: advances towards precision psychiatry.

INTRODUCTION: . Pharmacological treatment is the mainstay in the acute and long-term management of severe mental disorders such as major depressive di...

Bridging graph structure and knowledge-guided editing for interpretable temporal knowledge graph reasoning.

Temporal knowledge graph reasoning (TKGR) aims to predict future events by inferring missing entities with dynamic knowledge structures. Existing LLM-...

Artificial intelligence-associated delusions and large language models: risks, mechanisms of delusion co-creation, and safeguarding strategies.

Large language models (LLMs) are poised to become a ubiquitous feature of everyday life, mediating communication, decision making, and information cur...

Psychosis Risk and Generative Artificial Intelligence Use Frequency, Motivations, and Delusion-Like Experiences: Cross-Sectional Survey Study.

BACKGROUND: Growth of generative artificial intelligence (GenAI) has exploded in recent years. Many have noted its substantial potential to increase a...

Identification of metabolism and inflammation-related biomarkers and potential drugs for schizophrenia based on comprehensive bioinformatics and machine learning.

The cerebro-cerebellar system: Integrative roles in motor control, cognition, and neuropsychiatric disorders.

The cerebro-cerebellar system, a network of bidirectional loops between the cerebellum and cerebral cortex, is crucial for coordinating motor control,...

Mesocorticostriatal reinforcement learning of state representation and value with implications for the mechanisms of schizophrenia.

Mesocorticostriatal dopamine projections are crucial for value learning, motivational control, and cognitive functions. However, while dopamine's role...

Tomographic Foundation Model-FORCE: Flow-Oriented Reconstruction Conditioning Engine.

Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal impla...