Latest AI and machine learning research in information technology for healthcare professionals.
This paper introduces a novel framework that combines traditional centrality measures with eigenvalue spectra and diffusion processes for a more comprehensive analysis of complex networks. While centrality measures such as degree, closeness, and betweenness have been commonly used to assess nodal importance, they provide limited insight into dynamic network behaviors. By incorporating eigenvalue...
The exponential expansion of real-time data streams across multiple domains needs the development of effective event detection, correlation, and decision-making systems. However, classic Complex Event Processing (CEP) systems struggle with semantic heterogeneity, data interoperability, and knowledge driven event reasoning in Big Data environments. To solve these challenges, this research work pr...
Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic ...
Introduced in the early 2010s, Electronic Health Records (EHRs) have become ubiquitous in hospitals. Despite clear benefits, they remain unpopular a...
Large Vision-Language Models (LVLMs) are increasingly being explored for applications in telemedicine, yet their ability to engage with diverse pati...
The progress of artificial intelligence (AI) has made sophisticated methods available for cyberattacks and red team activities. These AI attacks can...
A significant and rising proportion of the global population suffer from non-communicable diseases, such as neurological disorders. Neurorehabilitat...
Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstruct...
Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple pla...
Advanced Persistent Threats (APTs) have caused significant losses across a wide range of sectors, including the theft of sensitive data and harm to ...
To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to ...
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rathe...
Backgrounds: Artificial intelligence (AI) is transforming healthcare, yet translating AI models from theoretical frameworks to real-world clinical a...
Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex int...
BACKGROUND: Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduc...
As various technologies are integrated and implemented into the food and agricultural industry, it is increasingly important for stakeholders throug...
The financial sector faces escalating cyber threats amplified by artificial intelligence (AI) and the advent of quantum computing. AI is being weapo...
Background: Patient recruitment in clinical trials is hindered by complex eligibility criteria and labor-intensive chart reviews. Prior research usi...
Efficient deployment of deep neural networks on resource-constrained devices demands advanced compression techniques that preserve accuracy and inte...
Despite their potential to address crucial bottlenecks in computing architectures and contribute to the pool of biological inspiration for engineeri...