Latest AI and machine learning research in surveillance for healthcare professionals.
Reproducibility challenges in bioimage analysis are common. The proliferation of tools and workflows creates a complex ecosystem that is difficult to reproduce without deliberate, explicit reporting and support. We assess the reproducibility of Gehrels et al.(2023) as a representative bioimage analysis workflow. We do not evaluate the biological claims; instead, we examine the reproducibility of t...
BACKGROUND: Psoriatic arthritis is a chronic inflammatory arthropathy that affects up to 22% of individuals with psoriasis. Identifying patients at higher risk of developing this disease presents a window of opportunity for dermatologists to consider treatments that can limit its progression. OBJECTIVES: This study aims to systematically identify and evaluate the predictive performance, risk of bi...
PURPOSE: This prospective multicenter study aimed to compare the decision-making abilities of board-certified colposcopists and two commercially avail...
INTRODUCTION: This systematic review evaluates the stage-specific diagnostic accuracy of artificial intelligence (AI) models for caries detection and ...
BACKGROUND: Digital twin (DT) systems have emerged as a promising approach in health care, enabling real-time, patient-specific virtual modeling and p...
OBJECTIVES: Pressure injuries are common chronic wounds that require accurate staging to guide management. Deep learning has shown promise for automat...
BACKGROUND: Estimation of lumbar spinal loads is important for understanding low back pain, guiding ergonomic interventions, and informing surgical an...
Although deep learning models have improved individual PET analysis, image processing, and quantification tasks, end-to-end automation from raw DICOM ...
Mosquito larval source management remains a critical strategy for malaria control, but traditional methods for mapping breeding sites are labor-intens...
Predicting the epidemic threshold [Formula: see text] in contact networks is a central challenge in computational epidemiology. Classical structural a...
OBJECTIVES: The application of large language models (LLMs) to systematic review tasks is rapidly expanding, yet the transparency and methodological r...
AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance d...
Personal health large language models (PH-LLMs) have rapidly evolved from research prototypes into consumer-facing, data-linked systems that support s...
BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity and hospitalization in children worldwide. In the post-COVID-19 era,...
BACKGROUND: Despite promising results of artificial intelligence (AI) in prostate cancer (PCa) detection, its impact on biparametric MRI (bpMRI) inter...
Diabetic Foot Ulcers are a severe complication of diabetes that can lead to infection, amputation, and increased mortality, making timely and objectiv...
Identifying amino acid changes that lead to phenotypic change is critical to viral surveillance. Common metrics, such as site-wise evolutionary rates ...
BACKGROUND: COPD remains a leading cause of global morbidity and mortality, with acute exacerbations driving disease progression and healthcare utilis...
BACKGROUND: Global infectious disease surveillance requires timely integration of heterogeneous data sources. Existing platforms typically address onl...
BACKGROUND: Large language models (LLMs) are being integrated into qualitative research processes, yet the scope, function, and reporting quality of t...