Latest AI and machine learning research in surveillance for healthcare professionals.
Rapid progress in vision-language modeling has enabled pathology report generation from gigapixel whole-slide images, but most approaches assume static training with simultaneous access to all data. In clinical deployment, however, new organs, institutions, and reporting conventions emerge over time, and sequential fine-tuning can cause catastrophic forgetting. We introduce an exemplar-free contin...
Screening mammography is high volume, time sensitive, and documentation heavy. Radiologists must translate subtle visual findings into consistent BI-RADS assessments, breast density categories, and structured narrative reports. While recent Vision Language Models (VLMs) enable image-to-text reporting, many rely on closed cloud systems or tightly coupled architectures that limit privacy, reproducib...
Background Accurate diagnostic tools are needed in schistosomiasis elimination settings to determine prevalence thresholds for assigning or stopping i...
Background: Although deep learning models have improved individual PET analysis, image processing and quantification tasks, end-to-end automation from...
Abstract Background Tuberculosis (TB) remains a major public health challenge in Nepal, with incidence rates substantially higher than global estimate...
Mobile genetic elements and genomic islands (GIs) frequently encode antibiotic resistance and host-adaptation cargo, yet routine genome comparison pip...
Societies are aging rapidly in parallel with the increasingly earlier onset of serious diseases in younger populations. These and other factors are cr...
Assessing the human infection potential of emerging coronaviruses remains a critical challenge for global health preparedness. In this study, we devel...
Microscopy images are frequently downsampled due to acquisition and computational constraints, requiring reconstruction before downstream analysis. Wh...
Accurately characterising mosquito infection dynamics is essential for effective dengue prevention and control, yet these dynamics are rarely observab...
Negation is a fundamental linguistic operation in clinical reporting, yet vision-language models (VLMs) frequently fail to distinguish affirmative fro...
Wastewater-based epidemiology provides a low-cost, scalable view of community infection dynamics, but converting these signals into actionable epidemi...
Antimicrobial resistance (AMR) is a growing global health crisis, responsible for an estimated 1.27 million deaths in 2019 alone. traditional approach...
Rapid advances in bioinformatics have transformed biomedical research in areas such as single-cell and spatial omics, digital pathology, and multi-mod...
The absence of pre-hospital physiological data in standard clinical datasets fundamentally constrains the early prediction of stroke, as patients typi...
Background: Traditional pharmacovigilance methods based on biostatistical approaches systematically exclude outliers and rare events, potentially miss...
Crowd scenes captured by cameras at different locations vary greatly, and existing crowd models have limited generalization for unseen surveillance sc...
Abstract Objective: To address the inefficiency, subjectivity, and high expertise barrier of traditional epidemiological causal inference, this study ...
Background The burden of new HIV infections and HIV-related deaths have declined dramatically in sub-Saharan Africa (SSA). However, current HIV survei...
BackgroundRapid emergence and replacement of SARS-CoV-2 variants underscore the need for early and reliable indicators of variant dominance to guide t...