Latest AI and machine learning research in schizophrenia for healthcare professionals.
INTRODUCTION/OBJECTIVES: General-purpose large language models (LLMs) have substantial limitations, including fabricated references and inconsistent concordance with evidence-based clinical practice guidelines (CPGs). OpenEvidence (OE) (Cambridge, MA) is a retrieval-augmented generation (RAG) platform designed to provide accurate, evidence-based, citation-supported answers. This study assessed the...
The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved safety and efficacy. To find possible DRD2 inhibitors, we developed an integrated in silico workflow in this study that combines drug-likeness filtering, machine learning-based quantitative structure-activity relationship (ML-QSAR) modelling, and struct...
ObjectivesLipedema is a chronic disorder characterized by pain and disproportionate fat distribution, and its diagnosis is frequently overlooked. The ...
OBJECTIVE: Antimicrobial Stewardship Programs (ASPs) need healthcare economic analyses to support and inform ASP strategies. This work aimed to determ...
Human social interactions rely on the ability to reflect on one's own and others' internal states and traits-a process known as mentalizing. Impaired ...
BACKGROUND: Large language models (LLMs) are increasingly integrated into healthcare applications, but their tendency to generate hallucinations-factu...
BACKGROUND: Large language models (LLMs) demonstrate potential in the laboratory, yet rigorous clinical evaluation remains limited. The opacity of LLM...
Current psychiatric diagnoses lack objective criteria, and this study aims to evaluate EEM as a potential tool for improving diagnostic objectivity ac...
BACKGROUND: While generative artificial intelligence (AI) is rapidly proliferating in healthcare research and clinical settings, there is a lack of ac...
OBJECTIVE: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using...
Artificial intelligence (AI) is rapidly reshaping clinical education by embedding assessment and feedback into everyday learning activities. Medical s...
BACKGROUND: This case describes a substance-induced manic episode with psychotic features in which interaction with an AI (artificial intelligence) ch...
The olfactory bulb (OB), the first central relay of the olfactory pathway, plays a critical role in odor perception and exhibits remarkable structural...
BACKGROUND: Adherence to guideline-based colonoscopy surveillance intervals remains suboptimal. Large language models (LLMs) show promise for automati...
As artificial intelligence becomes deeply embedded in scholarly practice, a critical and underexamined threat to research integrity has emerged: AI ha...
BACKGROUND: This scoping review evaluates the current state of generative artificial intelligence (AI) in implant dentistry, focusing on the performan...
OBJECTIVES: Artificial Intelligence (AI) is increasingly integrated into medicine, including otolaryngology. However, concerns remain regarding the ac...
In view of a pain point in the field of textual logic generation and physiological load quantification in physical education (PE) instructional design...
Magnetic resonance imaging (MRI) is widely regarded as the most reliable non-invasive imaging modality for detecting neurological disorders. However, ...
OBJECTIVE: To evaluate whether a custom agentic artificial intelligence (AI) pipeline can overcome the limitations of general-purpose large language m...