Latest AI and machine learning research in clinical trials for healthcare professionals.
Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required to achieve state-of-the-art (SOTA) performance. However, although Vision Transformers (ViTs) were introduced a few years ago, little is known about their texture recognition ability. Therefore, in this work, we introduc...
The growing deployment of large Vision-Language Models (VLMs) exposes critical safety gaps in their alignment mechanisms. While existing jailbreak studies primarily focus on VLMs' susceptibility to harmful instructions, we reveal a fundamental yet overlooked vulnerability: toxic text continuation, where VLMs produce highly toxic completions when prompted with harmful text prefixes paired with se...
While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning - including dise...
In recent years, the random vector functional link (RVFL) network has gained significant popularity in hyperspectral image (HSI) classification due ...
The convergence of edge computing and AI gives rise to Edge-AI, which enables the deployment of real-time AI applications and services at the networ...
In the field of conditional autonomous driving technology, driver perceived risk prediction plays a crucial role in reducing traffic risks and ensur...
The deployment of Large Language Models (LLMs) in robotic systems presents unique safety challenges, particularly in unpredictable environments. Alt...
We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our propose...
First-person video assistants are highly anticipated to enhance our daily lives through online video dialogue. However, existing online video assist...
Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structu...
To provide flexibility and low-level interaction capabilities, the unsafe tag in Rust is essential in many projects, but undermines memory safety an...
Estimating the volume of a convex body is a canonical problem in theoretical computer science. Its study has led to major advances in randomized alg...
Text-to-Image models may generate harmful content, such as pornographic images, particularly when unsafe prompts are submitted. To address this issu...
BACKGROUND: Adequate patient awareness and understanding of cancer clinical trials is essential for trial recruitment, informed decision making, and p...
Clinical trial eligibility matching is a critical yet often labor-intensive and error-prone step in medical research, as it ensures that participant...
In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying...
Understanding how urban environments are perceived in terms of safety is crucial for urban planning and policymaking. Traditional methods like surve...
Large language models (LLMs) hold substantial promise for clinical decision support. However, their widespread adoption in medicine, particularly in...
OBJECTIVES: Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT pr...
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers...