Latest AI and machine learning research in health policy for healthcare professionals.
The governance of frontier artificial intelligence (AI) systems--particularly those capable of catastrophic misuse or systemic failure--requires institutional structures that are robust, adaptive, and innovation-preserving. This paper proposes a novel framework for governing such high-stakes models through a three-tiered insurance architecture: (1) mandatory private liability insurance for front...
Optical microscopy contributes to the ever-increasing progress in biological and biomedical studies, as it allows the implementation of minimally invasive experimental pipelines to translate the data of measured samples into valuable knowledge. Within these pipelines, reliable quality assessment must be ensured to validate the generated results. Image quality assessment is often applied with ful...
The scarcity of accessible, compliant, and ethically sourced data presents a considerable challenge to the adoption of artificial intelligence (AI) ...
Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; how...
Online self-guided interventions appear efficacious for alleviating some mental health concerns. However, among persons who are offered online interve...
Artificial intelligence (AI) has played a novel role in aiding healthcare system functions and enhancing the patient experience. Multidisciplinary tea...
Given the exponentially growing volumes of genomic data, there are extensive efforts to accelerate genome analysis. We demonstrate a major bottlenec...
Modern sensing and monitoring applications typically consist of sources transmitting updates of different sizes, ranging from a few bytes (position,...
Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate u...
Image quality assessment (IQA) focuses on the perceptual visual quality of images, playing a crucial role in downstream tasks such as image reconstr...
Deep neural networks (DNNs) are susceptible to Universal Adversarial Perturbations (UAPs), which are instance agnostic perturbations that can deceiv...
Low-quality data often suffer from insufficient image details, introducing an extra implicit aspect of camouflage that complicates camouflaged objec...
Introduction: Bone health disorders like osteoarthritis and osteoporosis pose major global health challenges, often leading to delayed diagnoses due...
Learning-based denoising algorithms achieve state-of-the-art performance across various denoising tasks. However, training such models relies on acc...
In a context of constant increase in competition and heightened regulatory pressure, accuracy, actuarial precision, as well as transparency and unde...
Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic ...
Emotion recognition is crucial for advancing mental health, healthcare, and technologies like brain-computer interfaces (BCIs). However, EEG-based e...
Despite the excitement behind biomedical artificial intelligence (AI), access to high-quality, diverse, and large-scale data - the foundation for mo...
Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey method...
Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet comple...