Latest AI and machine learning research in schizophrenia for healthcare professionals.
Sparse-view CT reduces radiation dose and scanning time by acquiring fewer projection views, but angular undersampling makes reconstruction severely ill-posed, causing streak artifacts, structural blurring, and loss of fine details. Existing supervised methods are often tied to specific sampling settings, whereas generative methods may introduce anatomically inconsistent hallucination-like structu...
Multimodal Large Language Models (MLLMs) remain unreliable on fine-grained visual perception, even when high-resolution inputs preserve the necessary local details. We identify this limitation as visual context rot: decisive evidence may exist in the full image, yet fail to be reliably selected and used amid redundant visual context. We propose LOCUS (LOcal visual CUe Search), a training framework...
Large vision-language models (LVLMs) hallucinate: they assert visual details that the image does not support. A principled remedy is selective predict...
Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions a...
Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucinati...
Phage--host interaction (PHI) prediction is a fundamental problem in microbiology with applications in microbial ecology and microbiome engineering. E...
Interleaved thinking, where a unified multimodal model alternates between textual reasoning and visual generation, has shown promise on spatial and ph...
Real-world image super-resolution (SR) is often designed with a single restoration objective, despite the current capacity of generative models to pro...
Background: Concerns about "AI psychosis" have swirled in the media since ChatGPT's release, but few systematic analyses exist. We therefore conducted...
Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large mul...
Strabismus is a common ocular disorder that requires fine-grained subtype diagnosis for individualized treatment planning. However, existing deep lear...
Background: We previously examined the burden and predictors of sustained mental health care engagement in Ugandan first episode psychosis patients by...
Background: Traditional diagnostic models lack explainability, while multimodal language models prone to hallucination remain unsafe for medical educa...
Artificial intelligence (AI) tools have been rapidly adopted by medical researchers, yet whether early career researchers in low and middle income cou...
Background: The biomedical literature is expanding at an unprecedented rate, with over 4,000 new articles indexed on PubMed each day. Clinicians and r...
Background: Patients with CKD and polypharmacy face high rates of drug-related problems, yet comprehensive medication review remains time-intensive an...
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to...
Background: Accurate extraction of Human Phenotype Ontology (HPO) terms from clinical notes is essential for variant prioritization and genetic diagno...
Automated fetal ultrasound interpretation requires a workflow from visual perception, including plane recognition and anatomical segmentation, to clin...
Adolescence is a critical developmental window for the emergence of substance use and psychosis-spectrum symptoms, yet early risk for these outcomes r...