Schizophrenia

Ambient AI Scribes to Create Educational Feedback Notes for Medical Students: A Randomized Trial.

BACKGROUND: High-quality observation and feedback contribute to the development of clinical competence and professional growth in medical education. Faculty often struggle to translate verbal observations into written feedback because of documentation burden and competing demands. Ambient artificial intelligence (AI) scribes, already adopted in clinical practice, may address this challenge by capt...

Open-Source Large Language Models and AI Health Equity: A Health Service Triangle Model Perspective.

This study explores the role of open-source large language models (LLMs) in promoting artificial intelligence (AI) health equity from the perspective of the health service triangle model. First, it defines AI health, categorizes AI-supported decision-making patterns, and assesses the status quo of AI health inequalities. Second, by comparing open-source and closed-source LLMs in terms of patient p...

Artificial intelligence in emergency medicine: hallucination risks of large language models and mitigation approaches.

Characterizing executive dysfunctions in patients with schizo-obsessive comorbidity: comparing schizophrenia with obsessive-compulsive disorder.

BACKGROUND: The term 'schizo-obsessive comorbidity (SOC)' is used to describe the presence of obsessive-compulsive symptoms or obsessive-compulsive di...

A coarse-to-fine restoration framework integrating deep semantic inpainting and texture-aware refinement for Jinling school landscapes.

Traditional Chinese ink paintings on paper or silk are highly susceptible to degradation. Over time, physical decay such as creases not only damages t...

Increasing value in the Veterans Affairs Healthcare System (VA) with precision health: a continuing landmark collaboration with the Department of Energy.

OBJECTIVE: Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), l...

Using large language models to identify geriatric assessment domains in patients with advanced cancer: A feasibility study.

INTRODUCTION: In older adults with cancer, geriatric assessment (GA) can improve care quality. In-person assessment may not be feasible for all patien...

A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.

Some schizophrenia patients share characteristics with behavioral variant frontotemporal dementia (bvFTD) including gray matter volume (GMV) similarit...

EEG-based schizophrenia detection using handcrafted biomarkers and a TOA-optimized hybrid multi-branch CNN-Transformer framework.

Schizophrenia is a chronic psychiatric disorder for which electroencephalography (EEG) offers a low-cost, non-invasive window into abnormal neural dyn...

A Bibliometric Examination of EEGLAB Publications in Scopus and WoS Indexed Sources: A 20-Year Study of Asia-Pacific and Arabian Countries.

IntroductionEEGLAB is a widely used software for analyzing electroencephalography (EEG) datasets, with over 20 years of global use. This bibliometric ...

Omission and hallucination prevalence of clinical guidelines in diagnostic large language model outputs.

OBJECTIVE: Meaningful assessments of how large language models (LLMs) incorporate clinical guidelines require large-scale testing over many queries. H...

Neuroimaging-Based Subgroups in Schizophrenia: A Critical Appraisal of Clustering Studies.

Efforts to define biologically grounded subtypes of schizophrenia have increasingly leveraged neuroimaging data and clustering algorithms. Such approa...

Factors influencing disinformation avoidance behavior among generative artificial intelligence users: A cognition-affect-conation framework.

This study aimed to uncover the mechanisms driving disinformation avoidance behavior among generative artificial intelligence users to reduce negative...

Using Large Language Models to Identify Patient-Oncologist Communication Domains: A Feasibility Study.

BACKGROUND: The American Society of Clinical Oncology (ASCO) convened a multidisciplinary panel in 2017, resulting in patient-oncologist communication...

Physical mechanisms governing generalization and hallucination in deep learning for imaging through scattering media.

Deep learning has revolutionized computational imaging, yet its real-world deployment remains constrained by two critical challenges: poor generalizat...

A multimodal fNIRS-based machine learning model for symptom assessment and treatment response prediction in schizophrenia.

OBJECTIVE: Predicting early symptom severity and treatment response in schizophrenia is crucial for selecting optimal therapeutic strategies. This stu...

A comparative evaluation of EEG-based deep learning models for schizophrenia detection with cross-dataset validation and explainable AI.

OBJECTIVES: Schizophrenia is a neuropsychiatric disorder that affects emotional, behavioral, and brain functions that can be tracked using electroence...

Scenario-based evaluation of large language models for reference accuracy in dermatology: literature retrieval on latent tuberculosis in psoriasis patients on anti-IL-17/23 therapy.

BACKGROUND: Large language models (LLMs) could accelerate clinical literature searches, but their reliability is compromised by "hallucinations" gener...

Are routinely collected clinical and sociodemographic characteristics associated with social functioning and activities of daily living in schizophrenia? A machine learning approach descriptive of a schizophrenia cohort.

Patients with schizophrenia often experience substantial impairments in social functioning and activities of daily living (ADLs). Previous studies hav...

Multidisciplinary blinded randomized expert evaluation of large language models for clinical diagnosis and management.

BACKGROUND: Direct clinical uses of large language models (LLMs) remain controversial, partly because of the lack of methodological rigor in assessing...