Primary Care

SEMASEARCH Study Design: Real-World Evaluation of Semaglutide 2.4 mg in Adults With Severe Obesity Underrepresented in Clinical Trials.

BACKGROUND: Although semaglutide 2.4 mg has demonstrated significant weight loss efficacy in clinical trials, real-world data, particularly with regard to clinically complex and underrepresented populations, remain limited. OBJECTIVES: The study aims to assess the real-world effectiveness and patient-reported outcomes associated with the use of semaglutide 2.4 mg in individuals with severe and com...

Capacity building for ethical use of artificial intelligence in health: protocol for a scoping review of training initiatives and gaps in Africa.

INTRODUCTION: Artificial intelligence (AI) is increasingly embedded in health systems, with applications spanning diagnostic imaging, clinical decision support, disease surveillance and health system planning. While international guidance frameworks outline principles for safe and ethical AI deployment, effective governance depends on the capacity of regulators, research ethics committees, policym...

Retinal imaging and AI for non-invasive detection of diabetic macrovascular complications.

BACKGROUND: Diabetes mellitus accelerates vascular degeneration and increases the risk of major macrovascular complications, including Peripheral Arte...

Cardiovascular outcomes and subclinical atherosclerosis in metabolic-associated steatotic liver disease: long-term follow-up from a specialized outpatient cohort.

BACKGROUND AND AIMS: Cardiovascular disease is the main cause of mortality in metabolic-associated steatotic liver disease (MASLD). This study evaluat...

Explainable machine learning for long-term cardiovascular disease risk prediction in Chinese middle-aged and older adults: a 9-year longitudinal cohort study with web-based risk calculator.

Cardiovascular disease represents the leading cause of mortality in China, accounting for over 40% of all deaths. Existing risk prediction models pred...

Whole-body magnetic resonance imaging for cancer screening in asymptomatic adults: a multicenter study.

OBJECTIVES: Whole-body MRI is increasingly used for preventive health screening; however, the prevalence and distribution of incidental oncologically ...

Metagenomic profiling and predictive modeling of the gut microbiome reveal signatures of gestational disease.

The gut microbiome plays a vital role in maternal health and pregnancy outcomes, yet its impact on conditions like gestational hypertension (GH) and g...

Ten-Year Trends in Healthcare Use and Costs for People Living with Diabetes in France: Results from the Epicodiab Study Based on National Claims Data.

INTRODUCTION: Diabetes remains a global public health concern, with increased prevalence and a significant economic burden. Yet, most studies do not d...

Leveraging Machine Learning to Predict Mental Health Referral Follow-up Among US Military Personnel.

BACKGROUND: Noncompliance with mental health referrals among US military personnel remains a significant barrier to care. Operational deployments and ...

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and ...

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.

BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling e...

Can artificial intelligence bridge the gaps for primary diabetes care in low-income and middle-income countries?

Artificial intelligence (AI) has the potential to improve primary diabetes care in low-income and middle-income countries (LMICs), where the rising bu...

OptiSyn: an interpretable, multi-omics-driven graph convolutional network framework for synergy-oriented drug combination design in disease treatment.

BACKGROUND: Bioinformatics and large-scale computational modelling have emerged as essential research fields in modern biomedical science, enabling dr...

Research on Alzheimer's Disease Risk Assessment Models and Biomarker Screening Based on Bioinformatics Analysis and Machine Learning Algorithms.

INTRODUCTION: Alzheimer's Disease (AD) is among the most prevalent neurodegenerative disorders globally, yet effective early diagnostic strategies rem...

Development and validation of an interpretable machine learning model integrating inflammatory blood biomarkers for predicting postoperative breast cancer-related lymphedema: A prospective cohort study.

BACKGROUND: Breast cancer-related lymphedema is a frequent and debilitating complication. Early identification of high-risk individuals is critical, y...

Comparative evaluation of multimodal large language models for diagnostic accuracy in pediatric electrocardiography: a prospective comparative diagnostic accuracy study.

UNLABELLED: We evaluated three multimodal LLMs, ChatGPT (GPT-5.2), Gemini 3, and Microsoft Copilot, in pediatric ECG interpretation, focusing on clini...

Identifying Undiagnosed COPD Using Machine Learning-Based Classification.

BACKGROUND: COPD is a leading cause of morbidity and mortality worldwide. However, 80% of individuals with COPD are undiagnosed, leading to accelerate...

Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.

BACKGROUND: While artificial intelligence (AI)-assisted diagnostic software holds promise for improving diagnostic efficiency and reducing disparities...

Differential Factors Associated With the Presence of Persistent Symptoms in Individuals Diagnosed With Long COVID: Protocol for a Longitudinal Matched Case-Control Study.

BACKGROUND: Long COVID (postacute sequelae of SARS-CoV-2 infection) is a heterogeneous condition with persistent multisystem symptoms and substantial ...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...