Latest AI and machine learning research in schizophrenia for healthcare professionals.
Background: Large language models show promise for clinical decision support, yet their propensity for hallucination--generating plausible but unsupported claims--poses substantial patient safety risks. Retrieval-augmented generation (RAG) is widely assumed to mitigate this problem by grounding outputs in retrieved documents, but this assumption remains inadequately tested in clinical contexts whe...
Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different symptom profiles, potentially reflecting distinct underlying mechanisms. Identifying common patterns of symptoms using data-driven approaches could help clarify the heterogeneity of depression. Furthermore, examining the sociodemographic and lifestyle cha...
We introduce VIGIL (Visual Inconsistency & Generative In-context Lucidity), the first benchmark dataset and framework providing a fine-grained categor...
We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world...
Radiological diagnosis is a perceptual process in which careful visual inspection and language reasoning are repeatedly interleaved. Most medical larg...
Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending li...
Large Vision-Language Models (VLMs) have achieved remarkable success across diverse multimodal tasks but remain vulnerable to hallucinations rooted in...
Multimodal large language models (MLLMs) are increasingly adopted in remote sensing (RS) and have shown strong performance on tasks such as RS visual ...
The growing volume of video-based news content has heightened the need for transparent and reliable methods to extract on-screen information. Yet the ...
The robustness of Vision Language Models (VLMs) is commonly assessed through output-level invariance, implicitly assuming that stable predictions refl...
Open-vocabulary semantic segmentation (OVSS) extends traditional closed-set segmentation by enabling pixel-wise annotation for both seen and unseen ca...
In pharmacovigilance, analyzing drug safety cases is often time consuming due to the abundance of laboratory data, complex medical histories, and intr...
Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. Howeve...
We introduce a framework that automates the transformation of static anime illustrations into manipulatable 2.5D models. Current professional workflow...
Large multimodal reasoning models solve challenging visual problems via explicit long-chain inference: they gather visual clues from images and decode...
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...
Dopamine (DA) has been implicated in exploration-exploitation behaviour, i.e., exploring novel, potentiallybetter options vs. exploiting known, previo...
Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models generat...
Multimodal Large Language Models (MLLMs) have shown remarkable capability in assisting disease diagnosis in medical visual question answering (VQA). H...
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallu...