Latest AI and machine learning research in surveys for healthcare professionals.
Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Its applications span domains such as sports analysis, skill assessment, and medical care. Recent advances in AQA have introduced innovative methodologies, but similar methods often intertwine across different domains, highlighting the fragm...
Most of the ML datasets we use today are biased. When we train models on these biased datasets, they often not only learn dataset biases but can also amplify them -- a phenomenon known as bias amplification. Several co-occurrence-based metrics have been proposed to measure bias amplification between a protected attribute A (e.g., gender) and a task T (e.g., cooking). However, these metrics fail ...
Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annot...
Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across dive...
Internet connectivity in schools is critical to provide students with the digital literary skills necessary to compete in modern economies. In order...
Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in ima...
Machine learning (ML) algorithms have become integral to decision making in various domains, including healthcare, finance, education, and law enfor...
Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analys...
Over the past two decades, the Web Ontology Language (OWL) has been instrumental in advancing the development of ontologies and knowledge graphs, pr...
As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These m...
Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term ...
Personality assessment, particularly through situational judgment tests (SJTs), is a vital tool for psychological research, talent selection, and ed...
Disparities in access to healthcare have been well-documented in the United States, but their effects on electronic health record (EHR) data reliabi...
Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-wo...
In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead ...
With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtaine...
Large Language Models (LLMs) display formidable capabilities in generative tasks but also pose potential risks due to their tendency to generate hal...
Mitigating biases in computer vision models is an essential step towards the trustworthiness of artificial intelligence models. Existing bias mitiga...
Predictive machine learning (ML) models are computational innovations that can enhance medical decision-making, including aiding in determining opti...
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal infere...