Latest AI and machine learning research in schizophrenia for healthcare professionals.
AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often degrades due to loss of constraint focus, error accumulation, and memory-induced drift. This problem is especially visible in real-world deployments where context evolves, distractions are introduced, and decisions must remain consistent over time. A co...
Adverse events such as compulsory measures, absconding, illicit substance use, self-harm, aggressive behavior, and prolonged hospitalization pose significant challenges in forensic psychiatric inpatient care. This study introduces a machine learning-based tool to predict these events in patients with schizophrenia spectrum disorders (SSD) upon admission. Data from 370 court-mandated forensic inpat...
BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic feat...
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a nove...
We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,00...
Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination i...
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that...
Despite remarkable progress in image quality and prompt fidelity, text-to-image (T2I) diffusion models continue to exhibit persistent "hallucination...
Despite remarkable progress in image quality and prompt fidelity, text-to-image (T2I) diffusion models continue to exhibit persistent "hallucination...
BACKGROUND: The ultimate goal of successful schizophrenia treatment is not just to alleviate psychotic symptoms, but also to reduce distress and achie...
Diffusion models, originally developed for image generation, have emerged as a promising alternative to autoregressive large language models (LLMs)....
This work presents a comprehensive theory of consciousness grounded in mathematical formalism and supported by clinical data analysis. The framework...
Recent Large Vision-Language Models (LVLMs) have introduced a new paradigm for understanding and reasoning about image input through textual respons...
BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains largely unknown whether cross-modal conflict in sc...
BACKGROUND: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understan...
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relat...
Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatm...
BACKGROUND: The Aberrant Salience (AS) model conceptualizes psychosis onset as the altered attribution of salience to neutral stimuli. The Aberrant Sa...
The international refugee crisis deepens, exposing millions of dis placed children to extreme psychological trauma. This research suggests a com pac...
As the appearance of medical images is influenced by multiple underlying factors, generative models require rich attribute information beyond labels...