Latest AI and machine learning research in schizophrenia for healthcare professionals.
Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a critical challenge to achieving accurate visual understanding. To address this issue, we propose SECOND: Selective and Contrastive Decoding, a novel approach that enables VLMs to effectively leverage multi-scale visual information with an object-centric ...
Generative artificial intelligence (AI), with its increasing ubiquity and power, will likely transform forensic psychiatry, sparking both advances and new challenges for the field. A possible consequence of the technology is that it will be used to assist malingerers in learning about and feigning psychiatric symptoms. In this study, the AI chatbot ChatGPT was asked to provide information about th...
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologi...
Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia,...
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual diffe...
While multimodal large language models excel at various tasks, they still suffer from hallucinations, which limit their reliability and scalability ...
The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing appr...
Recent advancements in Multimodal Large Language Models (MLLMs) have enabled them to effectively integrate vision and language, addressing a variety...
Large Language Models (LLMs) hold immense promise for revolutionizing financial analysis and decision-making, yet their direct application is often ...
Recent research on generative models has primarily focused on creating product-ready visual outputs; however, designers often favor access to standa...
A central challenge in modern language models (LMs) is intrinsic hallucination: the generation of information that is plausible but unsubstantiated ...
Biomolecular networks, such as protein-protein interactions, gene-gene associations, and cell-cell interactions, offer valuable insights into the co...
Event Causality Identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely ...
Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -...
Large Visual Language Models (LVLMs) have demonstrated impressive capabilities across multiple tasks. However, their trustworthiness is often challe...
Computed tomography (CT) is a major medical imaging modality. Clinical CT scenarios, such as low-dose screening, sparse-view scanning, and metal imp...
Vision-language models (VLMs) aligned with general human objectives, such as being harmless and hallucination-free, have become valuable assistants ...
The expanding domain of digital mental health is transitioning beyond traditional telehealth to incorporate smartphone apps, virtual reality, and gene...
UNLABELLED: Generative artificial intelligence utilizing transformer technology is widely seen as a groundbreaking advancement in applied artificial i...
Hallucinations in large language models (LLMs) during summarization of patient-clinician dialogues pose significant risks to patient care and clinic...