Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 28,021 to 28,030 of 219,064 articles

Robust validation of neuroimaging and clinical models via the SAR method: A case study based on the ADNI dataset.

NeuroImage
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and substantial brain atrophy. Early and accurate prediction of disease progression and staging is crucial for timely intervention and effective t... read more 

AI-based mouse behavior analysis in pathology: A focus on movement disorders.

Neuroscience and biobehavioral reviews
Quantifying behavior in animal models is essential for understanding neurological disorders, yet traditional scoring methods often fail to capture the complexity and heterogeneity of motor dysfunction. Recent advances in artificial intelligence (AI) ... read more 

Functional cortical network alterations in Parkinson's disease with wearing-off revealed by resting-state fNIRS and graph theory.

Neuroscience
Wearing-off (WO) is a common motor complication in Parkinson's disease (PD), characterized by the re-emergence of symptoms before the next dose of dopaminergic medication and still lacking objective, bedside-available neurophysiological biomarkers. I... read more 

Clinical document metadata extraction: A scoping review.

Journal of biomedical informatics
OBJECTIVES: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heter... read more 

High-quality data selection-driven instruction tuning for biomedical large language models.

Journal of biomedical informatics
This study presents a novel data selection framework for enhancing the training efficiency of large language models (LLMs) in biomedical natural language processing (NLP) tasks. We focus on critical tasks sourced from the biomedical dataset, encompas... read more 

Time-of-flight secondary ion mass spectrometry research advances from a effective bibliometric method.

Analytical biochemistry
Due to the ability for high-resolution molecular imaging and chemical analysis with spatial resolution, time-of-flight secondary ion mass spectrometry (TOF-SIMS) is an extremely powerful surface analysis technique that is commonly used for chemical a... read more 

Deep learning-enabled ratiometric signal transduction for portable and intelligent colorimetric LAMP biosensing of Vibrio vulnificus.

Analytical biochemistry
Vibrio vulnificus is a highly lethal zoonotic pathogen associated with seafood consumption and aquaculture environments, demanding rapid and reliable on-site molecular diagnostics. Here, we report an intelligent biosensing platform utilizing a deep l... read more 

Integration of AHRR methylation, heavy metals, and clinical characteristics for urothelial carcinoma risk stratification: An explainable artificial intelligence approach.

Environmental pollution (Barking, Essex : 1987)
The short metabolic half-life of conventional tobacco biomarkers often limits their ability to reflect cumulative toxicological damage, which may complicate risk stratification for urothelial carcinoma (UC). This study explored an explainable artific... read more 

Engineering brain-penetrant PROTACs: Bridging molecular design and CNS delivery.

Advanced drug delivery reviews
The drug development for central nervous system (CNS) disorders, particularly neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and Huntington's disease, faces formidable challenges. While proteolysis-targeting chimeras (P... read more 

Knowledge, Attitudes, and Training Needs for AI in Primary Care: National Survey Study of Clinicians in the Veterans Health Administration.

JMIR formative research
BACKGROUND: Clinicians are the interface between artificial intelligence (AI) applications and patient care. To maximize benefits and minimize risks of AI, clinicians must be "AI-ready"-that is, willing and able to understand, evaluate, and appropria... read more