Primary Care

Diagnosis of shoulder dislocation on AP radiographs: A comparative analysis of diagnostic performance between orthopedic surgeons, emergency physicians, and ChatGPT models.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of ChatGPT in identifying acute shoulder dislocations and to compare its accuracy with that of orthopedic specialists and emergency medicine residents. METHODS: A total of 250 anteroposterior (AP) shoulder radiographs were included. All images were evaluated for the presence or absence of dislocation and for dislocation subtype (an...

Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

BACKGROUND: Several omics methods have been successfully used in hypertension prediction. However, the predictive ability of various multiomics data has not been compared in the same study sample, and it is unknown whether they provide additional predictive value over a good clinical risk factor score. METHODS: Clinical data augmented with modern multiomics methods (systolic blood pressure polygen...

Tumoroid-derived extracellular vesicles for prognosis and therapeutics: A valuable tool in precision medicine.

Precision medicine has transformed healthcare by tailoring treatment plans to an individual's genetic profile, in contrast to standardized therapies. ...

Artificial Intelligence Across the Obesity Continuum: From Mechanistic Insights to Global Precision Prevention and Therapy.

Obesity is a complex, rapidly escalating global health challenge that demands innovation across biology, clinical care, and public health. This review...

Clinical utility of an AI-based retinal imaging model for cardiovascular risk prediction in hypertensive retinopathy.

OBJECTIVE: This study presents an independent clinical evaluation of Dr.Noon CVD, a commercially developed artificial intelligence (AI)-based retinal ...

Transforming Gynecologic Cancer Care Through Artificial Intelligence: A Clinician's Guide to the Evolving Landscape.

Artificial intelligence (AI) is rapidly reshaping gynecologic oncology across the continuum of care. This clinician-focused review synthesizes current...

Machine learning-based binary classification of elevated HbA1c (≥6.5 %) for risk assessment.

OBJECTIVE: One of the most important biomarkers for evaluating long-term glycemic management and estimating the risk of diabetes is glycated hemoglobi...

Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.

Polygenic risk scores (PRSs) are becoming an important component of precision medicine, supporting earlier disease prediction and preventive strategie...

Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.

OBJECTIVE: The goal of this study was to curate a prostate MRI dataset from a screening population and to train and evaluate a deep-learning segmentat...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...

Self-reported periodontitis: A multiethnic community-based validation study.

OBJECTIVE: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation effort...

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with ...

Design of the first national lung cancer screening program in the European Union: the Croatian Model.

OBJECTIVES: To address Croatia's high lung cancer mortality and late-stage diagnoses, the Ministry of Health initiated a multidisciplinary effort to d...

An event-based filtering and weighted enhanced deep learning epileptic seizure prediction method.

The timely detection of impending seizures can offer physicians a critical window of opportunity to implement interventions and enable epileptic patie...

Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review.

BACKGROUND: Inpatients with diabetes have higher early unplanned readmission (EUR) rates. Diabetes management team (DMT) review reduces EUR. While glu...

Integrating administrative health data and machine learning to predict ALS onset.

BACKGROUND: This study aims to develop a Machine Learning (ML) model to predict the initial diagnosis of Amyotrophic Lateral Sclerosis (ALS). METHODS:...

Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms.

BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit...

Lightweight Seizure Prediction Model based on Kernel-Enhanced Global Temporal Attention.

Clinically, epilepsy manifests as a chronic condition marked by unprovoked, recurrent seizures, plaguing over 70 million individuals with debilitating...

Precision engineering of anti-atherosclerotic herbal nanomedicine: from machine learning-aided active components screening to optimized metal-phenolic network codelivery.

Atherosclerosis (AS), a chronic inflammatory disease linked to oxidative stress and lipid imbalance, remains a major cardiovascular threat. Traditiona...

A machine learning based framework for identifying consumer product injuries from social media data.

BACKGROUND: Safety is a critical aspect of consumer products. However, there are millions of product related injuries reported each year. Traditional ...