Diet & Nutrition

The illusion of competence: Evaluating the clinical reasoning of large language models in pediatric gastroenterology.

OBJECTIVE: To evaluate the diagnostic accuracy and clinical reasoning of three frontier large language models (LLMs) across standardized pediatric gastroenterology, hepatology, and nutrition (PGHN) clinical vignettes. METHODS: In this cross-sectional study, 25 fictional PGHN vignettes were developed by one board-certified pediatric gastroenterologist and evaluated using three LLMs: Gemini 3.1 Pro,...

Machine Learning Analysis of Sex Differences in Cardiovascular-Kidney-Metabolic Risk Factors and Prognosis Among Patients With Moderate-to-Severe Coronary Artery Calcification: Prospective Cohort Study.

BACKGROUND: Patients with coronary artery calcification exhibit notable sex differences in clinical presentation, particularly concerning the role of cardiovascular-kidney-metabolic (CKM) risk factors and their impact on prognoses. However, the precise nature of these sex-specific differences remains incompletely understood. OBJECTIVE: This study aimed to investigate sex disparities in CKM risk fa...

The Triglyceride Glucose-a Body Shape Index: A Novel Composite Index for Prediction of Periodontitis Risk.

INTRODUCTION AND AIM: Insulin resistance and obesity are significant metabolic risk factors for periodontitis. This study aimed to systematically inve...

Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.

BACKGROUND: Large language models (LLMs) are increasingly embedded in conversational agents for cardiometabolic care. These systems could support self...

Biomarkers in predicting mortality in hemodialysis patients: recent advances.

Patients undergoing maintenance hemodialysis face annual mortality rates of 15-27%, with cardiovascular causes accounting for more than half of all de...

Future of CT body composition research: Methodological discrepancies and advances.

Body composition research utilizing computed tomography (CT) has increased over the past several decades as researchers use clinically acquired CT for...

Explainable Machine-Learning Model for Predicting Severe Obstructive Sleep Apnea in Patients Undergoing Metabolic Bariatric Surgery.

BACKGROUND: Severe obstructive sleep apnea (SOSA) is associated with an increased risk of perioperative complications in patients undergoing metabolic...

Interpretable machine learning models predict cognitive impairment in adults aged 50 and over with diabetes: influence of dietary nutrients.

Introduction: diabetes mellitus increases the risk of cognitive impairment, but the role of dietary nutrients remains unclear. Objectives: to develop ...

Spheroids with Artificial Mineral Organelles: Machine-Learning-Assisted Analysis and Control of Mechanical Properties, Fusion, and Ossification.

Controlling the balance between structural integration and functional maturation of microtissues remains a central challenge in bone tissue engineerin...

Integration of metabolomics and machine learning algorithm for discovery of early diagnostic biomarkers of osteoporosis.

BACKGROUND: Osteoporosis (OP) is a prevalent metabolic bone disorder and a major public health concern characterized by reduced bone mass and bone mic...

UniFFBench: evaluating universal machine learning force fields against experimental measurements.

Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodi...

Mineral biosignature identification from Raman spectroscopy using machine learning.

Biosignature detection remains a key challenge in astrobiology, yet robust mineral biosignatures remain limited. Raman spectroscopy is increasingly ap...

Calibrated Weighted Rank Aggregation for Virtual Screening Independently Rediscovers Privileged Vitamin D Receptor Ligand Scaffolds.

Computational drug discovery relies on virtual screening to prioritize a small number of compounds for experimental testing, yet combining heterogeneo...

Frailty burden and symptomatic BPH/LUTS in aging men: evidence from two nationally representative population-based studies.

BACKGROUND: The frailty index (FI) is a well-established marker of biological aging and a widely validated predictor of adverse health outcomes in old...

Integration of inflammatory and nutritional biomarkers with machine learning enhances prediction of progesterone response in fertility-preserving endometrial carcinoma management.

BACKGROUND: Endometrial carcinoma (EC) and atypical endometrial hyperplasia (AEH) increasingly affect young women, posing challenges for fertility pre...

Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.

This systematic review evaluated traditional machine learning (TML) and deep learning (DL) approaches for obesity prediction in longitudinal studies a...

Machine learning-based evaluation of lipid biomarkers for cardiovascular risk prediction in chronic kidney disease: A cross-sectional study.

Chronic kidney disease (CKD) is an important public health issue globally, greatly increasing the prevalence and mortality of cardiovascular disease (...

Quality and nutritional accuracy of mHealth nutrition apps in Türkiye: A validation study using food records.

Background: Mobile health (mHealth) applications are increasingly used for dietary assessment and self-monitoring, yet their validity and comprehensiv...

Artificial Intelligence for Evidence Synthesis of Emerging Biologics to Improve Skeletal Health in Osteogenesis Imperfecta: Systematic Review and Meta-Analysis.

BACKGROUND: Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demons...

Revolutionizing Nutrition: AI-Driven Health Solutions and Personalized Diets.

Artificial Intelligence (AI) is revolutionizing health and nutrition, significantly advancing personalized dietary management and health optimization....