Latest AI and machine learning research in schizophrenia for healthcare professionals.
Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowle...
Phage--host interaction (PHI) prediction is a fundamental problem in microbiology with applications in microbial ecology and microbiome engineering. Existing computational approaches typically convert phage and host information into numerical representations derived from sequence similarity, protein content, genome composition or reference databases, then score candidate hosts or train host-predic...
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...
Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the cur...
Deep learning-based structure prediction enables the design of peptide ligands without relying on naturally occurring scaffolds. However, most computa...
Motivation: The ToxCast database is a valuable resource for computational toxicology and new approach methodologies (NAMs), but the approximately 100G...
Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and eval...