Latest AI and machine learning research in preventive care for healthcare professionals.
Automated polyp segmentation in colonoscopy continues to pose challenges due to substantial appearance variations and indistinct polyp boundaries. Although emerging foundation models (FMs) such as DINOv2, SAM, and OneFormer, demonstrate remarkable generalization capabilities, their direct transfer to the polyp segmentation task and deployment in real-time clinical settings are difficult due to lac...
Reverse vaccinology has enabled sequence-based antigen discovery, but it overlooks the rich semantic knowledge embedded in the biomedical literature. Here we establish Semantic Vaccinology (SemVac), a paradigm that leverages large language models (LLMs) to predict protective antigens directly from scientific text. Benchmarking 14 state-of-the-art LLMs on a curated antigen dataset shows that text-r...
Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated...
Reliable AI for screening mammography requires training data representative of the low cancer prevalence and subtle abnormalities found in screening p...
Large-scale gas-network scenario evaluation is a computational bottleneck in integrated energy-system planning, particularly when gas infrastructure i...
Vision Transformer (ViT) has been widely used as a powerful framework for modeling global dependencies among image patches. However, its core componen...
Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow...
Early screening of chronic kidney disease (CKD) is essential for preventing irreversible progression; however, many machine learning (ML)-based screen...
Tuberculosis (TB) is a major global health challenge, with many cases remaining undiagnosed due to limited access to screening and diagnostic services...
Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises fr...
Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recen...
Blood-based biomarkers discovered by machine learning often lack disease specificity and cross-population robustness for clinical applications. We des...
Progress in colonoscopy polyp segmentation is routinely reported through leaderboard comparisons on a small set of public benchmarks. We argue that th...
Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns...
Depression screening from large-scale behavioral data is challenged by fragmented circadian indicators, limited interpretability, and the lack of inte...
Modern GPU domain-specific languages (DSLs), such as Triton and TileLang, are increasingly used to implement specialized deep-learning kernels and as ...
Background: Despite advances in circulating tumor DNA analysis, reliable detection of oncological disease from ultra-low coverage whole genome sequenc...
Introduction. Systematic reviews are essential for informing health policy and practice. Artificial intelligence (AI) automates the article screening ...
Ground reaction force (GRF)-based gait analysis provides objective, non-invasive evidence for neurological and musculoskeletal assessment, but its tra...
Background: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently mi...