Latest AI and machine learning research in surveys for healthcare professionals.
Vision-Language Models (VLMs) are known to inherit and amplify societal biases from their web-scale training data with Indian being particularly misrepresented. Existing fairness-aware datasets have significantly improved demographic balance across global race and gender groups, yet they continue to treat Indian as a single monolithic category. The oversimplification ignores the vast intra-nationa...
Digital subtraction angiography (DSA) plays a central role in the diagnosis and treatment of cerebrovascular disease, yet its invasive nature and high acquisition cost severely limit large-scale data collection and public data sharing. Therefore, we developed a semantically conditioned latent diffusion model (LDM) that synthesizes arterial-phase cerebral DSA frames under explicit control of anatom...
Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are ...
Ductular Reactions (DRs) are dynamic and complex multicellular responses that occur as a result of various hepatic injuries. Precise identification an...
Neural representations rely on the ability of neuronal assemblies to display organized spiking patterns, despite being embedded within noisy networks....
Epidemiologists have access to various methods to reduce bias and improve statistical efficiency in effect estimation, from standard multivariable reg...
Background: Generating synthetic data using artificial intelligence, such as large language models (LLMs), is a useful strategy in public health becau...
Compressing long chains of thought (CoT) into compact latent tokens is crucial for efficient reasoning with large language models (LLMs). Recent studi...
Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation an...
Large Language Models (LLMs) demonstrate strong performance at medical specialty board multiple-choice question (MCQ) answering, however, underperform...
Medical image classification is a core task in computer-aided diagnosis (CAD), playing a pivotal role in early disease detection, treatment planning, ...
The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent...
Automated respiratory sound classification supports the diagnosis of pulmonary diseases. However, many deep models still rely on cycle-level analysis ...
Endoscopic Retrograde Cholangiopancreatography (ERCP) is a key procedure in the diagnosis and treatment of biliary and pancreatic diseases. Artificial...
Robust machine learning for regulatory genomics is studied under biologically and technically induced distribution shifts. Deep convolutional and atte...
Vision-Language-Action (VLA) models have shown promise in robot manipulation but often struggle to generalize to new instructions or complex multi-tas...
Ecotoxicological tests with soil organisms, such as the collembolan Folsomia candida, are essential for assessing chemical risks in terrestrial ecosys...
Object detection in sonar images is a key technology in underwater detection systems. Compared to natural images, sonar images contain fewer texture d...
Explainable Artificial Intelligence (XAI) techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM), have become indispensable for vis...
Prior research demonstrates that performance of language models on reasoning tasks can be influenced by suggestions, hints and endorsements. However, ...