Latest AI and machine learning research in surveys for healthcare professionals.
Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluster assignments, without indicating assignment reliability, modality contributions, or why a domain decision should be trusted. We present OmicSync, a reliability-aware spatial multi-omics framework that couples unsupervis...
Background. Phthalates are hypothesised to act as metabolic disruptors, and machine learning applied to the National Health and Nutrition Examination Survey (NHANES) has become a common approach to testing such associations. Because urinary phthalate metabolites are measured only in a one-third laboratory subsample, these analyses face a large deliberate gap in exposure data, a structure that invi...
Promptable segmentation models provide a reusable interface, but direct transfer to automatic infrared small-target segmentation (IRSTD) exposes a mis...
Background and Objectives: Subjective cognitive decline (SCD), self-reported worsening confusion or memory over the past year, is a common early marke...
Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated se...
Background: Low birth weight remains a primary driver of neonatal and infant mortality in Ethiopia. Machine learning models can assist early risk iden...
Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inherent characte...
Analog gauges remain common in industrial environments where manual inspection is costly or hazardous. The engineering application addressed here is d...
Abstract Background Breast and cervical cancer screening in Ghana remains low, and several analyses of the Ghana Demographic and Health Survey (GDHS) ...
Background: The growing burden of lifestyle-related chronic diseases has increased the need for clinically interpretable decision-support tools capabl...
Objective. Formal large language model (LLM) evaluations score isolated prompts, but clinicians and health-informatics researchers meet model failures...
Background: Generative AI is evolving at a rapid pace, and many individuals are utilizing chatbots for mental health support. The safety of chatbots a...
Stigmatizing language in clinical documentation, which conveys negative stereotypes, attitudes, or judgments toward patients, is a recognized source o...
Modern transmission electron microscopes are versatile instruments which have become indispensable tools for understanding structure and chemical comp...
Deep networks segment brain tumours accurately in-distribution, but can fail silently when the input differs from their training data. That risk is ce...
Existing vision-language model (VLM) benchmarks emphasize perception and reasoning accuracy (how well VLMs describe and reason about what they see in ...
Machine learning ethics researchers and critical HCI scholars have argued that algorithmically predicting gender is wrong. At the same time, other res...
Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not expl...
Low-light imaging often introduces color bias caused by the low signal-to-noise ratio and the image formation process. Although recent low-light image...
Visible-infrared object detection relies on complementary RGB and thermal cues, but its performance is often degraded by cross-modal spatial misalignm...