Latest AI and machine learning research in preventive care for healthcare professionals.
Accurate, affordable tuberculosis (TB) diagnostics that do not require sputum samples are urgently needed for TB control and elimination. Prior serological TB tests have failed due to low accuracy in diagnosing active TB (ATB) in endemic areas where baseline seropositivity, due to latent infection (LTBI) or vaccination, is common. We combined high-throughput sample-sparing antibody-omics with mach...
Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general population, but their impact on care is unclear. The SPOT study is an investigator-initiated and -designed, unblinded, randomized controlled trial. Participants from a Dutch non-profit health insurance living in and around region Rotterdam the Netherlands, ...
Prior work showed that state-of-the-art (mid-2025) large language models (LLMs) prompted with varying batch sizes can perform well on systematic revie...
Large language models (LLMs) are increasingly used in randomized clinical trial (RCT) screening, but their potential for sociodemographic bias remains...
The 2022 global outbreak of clade IIb mpox represented a turning point in public health’s handling of poxviruses. The primary vaccine available for pr...
Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, especially during abstract screening. This study evalua...
Colorectal cancer (CRC) remains a major cause of cancer-related morbidity and mortality worldwide. Endoscopy and adenoma removal are effective in redu...
Colorectal cancer is the third leading cause of cancer-related deaths in the United States, and colonoscopy remains the gold standard for early detect...
Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic...
Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...
Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying a...
This study aimed to design and evaluate an explainable machine learning (ML) framework that integrates sensor-based motor assessments with demographic...
Hypertension is a silent killer, with over half of affected adults unaware of their condition1,2. This lack of awareness is a major concern, as early ...
Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed condit...
This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...
Sex differences in the humoral immune responses to the seasonal quadrivalent influenza vaccine (QIV) in young adults (YA; 18-49yo) or high dose QIV in...
Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...
Proteome-guided liquid biopsy tests hold immense promise for the future of early cancer detection. Our previous published work has shown strong perfor...
Low-dose computed tomography (LDCT) lung cancer screening has significantly enhanced early detection and patient survival rates in the population at r...
INTRODUCTION: This paper presents a proposal for the modelling and reference architecture of a digital twin for immunisation services in primary healt...