Artificial Intelligence Medical Compendium

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

Showing 52,281 to 52,290 of 225,279 articles

Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches for Mycobacterium abscessus Subspecies Identification.

Journal of proteome research Feb 9, 2026
Distinguishing Mycobacterium abscessus subspecies presents significant diagnostic challenges due to their genetic homogeneity and variability in analytical platforms. Our research combines matrix-assisted laser desorption/ionization time-of-flight (M... read more 

Real-world evaluation of large language models in detecting drug-related problems: A clinical pharmacist-AI concordance study in hematology care.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners Feb 9, 2026
IntroductionLarge language models (LLMs) offer potential as clinical decision support systems (CDSS) for detecting drug-related problems (DRPs), yet their real-world performance compared to clinical pharmacists (CPs) remains unclear, especially in co... read more 

Inferring context-specific site variation with evotuned protein language models.

NAR genomics and bioinformatics Feb 9, 2026
Multiple sequence alignments (MSAs) have been traditionally used for making inferences about site-specific diversity in proteins. Recent advancements in the field of artificial intelligence have highlighted the potential of protein language models (p... read more 

Artificial intelligence in trauma care: applications, ethical challenges, and pathways toward responsible integration.

Current opinion in anaesthesiology Feb 9, 2026
PURPOSE OF REVIEW: Artificial intelligence is increasingly applied across the trauma care continuum, from prehospital triage to in-hospital decision-making. This review provides a timely synthesis of emerging applications, ethical challenges, and reg... read more 

Evaluation of an automated sleep apnea scoring algorithm via the Wesper Lab home sleep apnea test.

Sleep medicine Feb 9, 2026
This study evaluated the performance of the Wesper Lab home sleep apnea test (HSAT) artificial intelligence (AI) automated scoring algorithm under both in-laboratory and real-world conditions. We conducted a multi-tiered validation using two datasets... read more 

Artificial Intelligence-based analysis of pre-eclampsia gene expression profiles for identification of novel potential pre-eclampsia diagnostic biomarkers.

Pregnancy hypertension Feb 9, 2026
Preeclampsia (PE) is a multifactorial and heterogeneous hypertensive disorder of pregnancy that poses significant diagnostic and therapeutic challenges. Identifying robust and generalizable biomarkers is critical for early detection and improved clin... read more 

Multi-structure CT radiomics-based consensus model for the diagnosis of pancreatic ductal adenocarcinoma and vascular involvement.

Computers in biology and medicine Feb 9, 2026
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy, with accurate preoperative assessment of vascular involvement critical for determining resectability and treatment planning. Conventional contrast-enhanced CT relies o... read more 

Periodontal bone loss analysis via keypoint detection with heuristic post-processing.

Computers in biology and medicine Feb 9, 2026
OBJECTIVES: This study proposes a deep learning framework and an annotation methodology for the automatic detection of periodontal bone loss landmarks, associated conditions, and staging. Methods192 periapical radiographs were collected and annotated... read more 

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

Computers in biology and medicine Feb 9, 2026
OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Unfortunately, current diagnostic techniques are often invasive and expensive. Our research focuses on... read more 

Machine learning reveals molecular substructure drivers of organic contaminant translocation in crops.

The Science of the total environment Feb 9, 2026
Understanding the translocation of organic contaminants in crops is vital for food safety and human health. This study developed machine learning (ML) models to predict root-to-stem translocation factors (TF) and identify molecular substructures infl... read more 
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