Latest AI and machine learning research in public health & policy for healthcare professionals.
Neural networks excel as function approximators, but their complexity often obscures the nature of the functions they learn. In this work, we propose the linearity score $\lambda(f)$, a simple and interpretable diagnostic that quantifies how well a regression network's output can be mimicked by a linear model. Defined as the $R^2$ between the network's predictions and those of a trained linear s...
BACKGROUND: Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold-standard hyperinsulinemic-euglycemic clamp has limitations in practical application. The development of more convenient and efficient methods to predict a...
We present SLICK, a novel framework for precise and robust car damage segmentation that leverages structural priors and domain knowledge to tackle r...
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Cur...
When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture ...
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillanc...
OBJECTIVES: Training public health personnel is crucial for enhancing the capacity of public health systems. However, existing research often falls sh...
The low-altitude economy is emerging as a key driver of future economic growth, necessitating effective flight activity surveillance using existing ...
Real-world surveillance often renders faces and license plates unrecognizable in individual low-resolution (LR) frames, hindering reliable identific...
For more than 60 years, artificial intelligence (AI) has served as a mainstay in augmenting and assisting the lives of individuals across a wide array...
Most post-disaster damage classifiers succeed only when destructive forces leave clear spectral or structural signatures -- conditions rarely presen...
Surveillance systems play a critical role in security and reconnaissance, but their performance is often compromised by low-quality images and video...
This position paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient tes...
Person Re-Identification (Re-ID) is a very important task in video surveillance systems such as tracking people, finding people in public places, or...
Rapidly evolving viruses use antigenic drift as a key mechanism to evade host immunity and persist in real populations. While traditional models of ...
Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could signif...
Breast cancer remains a leading global health concern, with significant strides made in early detection and treatment. However, effective long-term su...
Telesurgery, or remote surgery, represents a transformative fusion of medicine and technology, enabling surgeons to perform procedures on patients loc...
This statement conveys the European Society of Gastrointestinal Endoscopy (ESGE) position on the use of computer-aided detection (CADe) with artificia...
The early detection of high-risk human papillomavirus (HR-HPV) is crucial for the assessment and improvement of prognosis in cervical cancer. However,...