Schizophrenia

The role of AI-driven communication in delirium prevention, detection, and care for critically ill ICU patients: A systematic review with inductive thematic synthesis.

BACKGROUND: Delirium remains one of the most consequential complications among critically ill patients in ICUs, exerting profound effects on morbidity, mortality, and annual healthcare costs exceeding $81 billion. Communication barriers between sedated or mechanically ventilated patients, their families, and multidisciplinary teams frequently delay recognition and impair management of delirium. Th...

Artificial intelligence in psychiatry: Current and emerging trends, clinical applications, and research gaps explored through a bibliometric analysis.

OBJECTIVE: The aim of this study is to analyze AI-based scientific publications in psychiatry using bibliometric methods to identify prominent themes, research trends, and future opportunities. METHODS: Original articles published between 1980 and 2025 and indexed in the "Psychiatry" category of the Web of Science were screened. The search strategy included keywords such as "artificial intelligenc...

Enlarged cavum septum pellucidum & increased incidence of schizophrenia: Narrative review for neurodevelopmental correlate.

Schizophrenia being a major psychiatric disorder comprises of dominant neurodevelopmental corroborations; still there are inadequate markers which rev...

Improving systems & communities of psychosis care: Proceedings from the 2024 Mass-STEP Conference.

A 2024 conference titled "Early Intervention in Psychosis: Systems & Communities of Care" brought together researchers, clinicians, policy experts, an...

Vision-language models in diagnostic imaging: review of technical advances, clinical validation, and practical deployment.

BACKGROUND: Radiology faces an unprecedented workload crisis, creating demand for AI solutions to enhance efficiency and quality. Vision-language mode...

Using large language model to aid in teaching medical imaging report writing.

PURPOSE: This study aims to compare several free large language models (LLMs), identify which provides the most effective feedback, and investigate wh...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Machine learning analysis of clinical, psychological and sociodemographic factors predicting mental well-being in patients with severe mental illness: Insights from the French REHABase cohort.

PURPOSE: Mental well-being is a cornerstone of recovery for people with mental disorders. Unfortunately, despite many studies on the topic, the litera...

Analysis of language patterns in schizophrenia based on natural language processing.

BACKGROUND: Schizophrenia is a severe mental disorder, with language dysfunction being one of its prominent features. Studies have shown that language...

Intelligent electroencephalogram feature engineering for rapid mental health diagnosis.

Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that af...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

MedSumGraph: enhancing GraphRAG for medical QA with summarization and optimized prompts.

The rapid development of large language models (LLMs) has accelerated research into applying artificial intelligence (AI) to domains such as medical q...

Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.

Language disturbances are central features of serious mental illnesses, yet traditional clinical assessments often rely on subjective evaluation that ...

Machine learning identifies traumatic experiences as being associated with auditory verbal hallucinations in both a non-clinical population and individuals diagnosed with psychosis.

BACKGROUND: Auditory verbal hallucinations (AVHs) are a core symptom of psychosis but their prevalence in the general population ranges from 5-28 %. M...

Detecting psychosis via natural language processing of social media posts: potentials and pitfalls.

The early detection and continuous monitoring of psychosis is of utmost importance in ensuring timely and effective treatment. Current mental health c...

Can large language models approximate the results of meta-analyses in critical care? A meta-research study.

BACKGROUND: Large language models (LLMs) are capable of processing extensive textual data and synthesizing evidence to answer complex clinical questio...

Effective alternative strategies to combat challenges associated with MDR bacterial infections: Drug repurposing, role of artificial intelligence, and novel therapeutic options.

Antibiotic failure has emerged as a critical global health concern, driven by a combination of factors including the stagnation in the discovery of ne...

Performance of Large Language Models on Radiology Residency In-Training Examination Questions.

RATIONALE AND OBJECTIVES: Large language models (LLMs) are increasingly investigated in radiology education. This study evaluated the performance of s...

Machine learning prediction of suicide attempts in major depression: Feature selection and model development using national survey data.

BACKGROUND: The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD). METHODS: Cross-se...

Uncovering predictors of bipolar II conversion to bipolar I: A machine learning analysis of national health records in Taiwan.

BACKGROUND: Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited resea...