Latest AI and machine learning research in pneumonia for healthcare professionals.
The practice of pharmacovigilance relies on large databases of individual case safety reports to detect and evaluate potential new causal associations between medicines or vaccines and adverse events. Duplicate reports are separate and unlinked reports referring to the same case of an adverse event involving a specific patient at a certain time. They impede statistical analysis and mislead clini...
Study Design: The study outlines the development of an autonomous AI system for chest X-ray (CXR) interpretation, trained on a vast dataset of over 5 million X rays sourced from healthcare systems across India. This AI system integrates advanced architectures including Vision Transformers, Faster R-CNN, and various U Net models (such as Attention U-Net, U-Net++, and Dense U-Net) to enable compre...
Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opacities in CXRs frequently...
Exploration of the impact of personality traits on social interactions within anonymous online communities poses a challenge at the interface of net...
Document Layout Analysis (DLA) is a fundamental task in document understanding. However, existing DLA and adaptation methods often require access to...
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and ...
Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across dat...
Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases suc...
Accurate and reliable image classification is crucial in radiology, where diagnostic decisions significantly impact patient outcomes. Conventional d...
Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges,...
As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown...
The AI community usually focuses on "how" to develop AI techniques, but lacks thorough open discussions on "why" we develop AI. Lacking critical ref...
This study introduces the SHAP-integrated convolutional diagnostic network (SICDN), an interpretable feature selection method designed for limited d...
We present Autoregressive Representation Alignment (ARRA), a new training framework that unlocks global-coherent text-to-image generation in autoreg...
Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To addre...
Artificial intelligence has shown the potential to improve diagnostic accuracy through medical image analysis for pneumonia diagnosis. However, trad...
Developing advanced medical imaging retrieval systems is challenging due to the varying definitions of `similar images' across different medical con...
The COVID-19 pandemic has profoundly affected the normal course of life -- from lock-downs and virtual meetings to the unprecedentedly swift creatio...
Computed tomography (CT) is a key imaging modality for diagnosis, yet its clinical utility is marred by high radiation exposure and long turnaround ...
According to the World Health Organization (WHO), pneumonia is a disease that causes a significant number of deaths each year. In response to this i...