Latest AI and machine learning research in ethics for healthcare professionals.
The use of Natural Language Processing (NLP) in highstakes AI-based applications has increased significantly in recent years, especially since the emergence of Large Language Models (LLMs). However, despite their strong performance, LLMs introduce important legal/ ethical concerns, particularly regarding privacy, data protection, and transparency. Due to these concerns, this work explores the us...
In the domain of algorithmic decision-making, non-Markovian dynamics manifest as a significant impediment, especially for paradigms such as Reinforcement Learning (RL), thereby exerting far-reaching consequences on the advancement and effectiveness of the associated systems. Nevertheless, the existing benchmarks are deficient in comprehensively assessing the capacity of decision algorithms to ha...
Mean-field characterizations of first-order iterative algorithms -- including Approximate Message Passing (AMP), stochastic and proximal gradient de...
While Medical Large Language Models (MedLLMs) have demonstrated remarkable potential in clinical tasks, their ethical safety remains insufficiently ...
Data brokers collect and sell the personal information of millions of individuals, often without their knowledge or consent. The California Consumer...
As the deployment of large language models (LLMs) grows in sensitive domains, ensuring the integrity of their computational provenance becomes a cri...
Agents based on Large Language Models (LLMs) have demonstrated strong capabilities across a wide range of tasks. However, deploying LLM-based agents...
With the advent of Industry 5.0, manufacturers are increasingly prioritizing worker well-being alongside mass customization. Stress-aware Human-Robo...
Foundation models are revolutionising pathology by leveraging large-scale, pretrained artificial intelligence (AI) systems to enhance diagnostics, aut...
As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be pai...
To ensure a balance between open access to justice and personal data protection, the South Korean judiciary mandates the de-identification of court ...
Privacy-preserving neural network training in vertically partitioned scenarios is vital for secure collaborative modeling across institutions. This ...
Electroencephalography (EEG) is extensively employed in medical diagnostics and brain-computer interface (BCI) applications due to its non-invasive ...
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue orga...
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue orga...
Artificial intelligence (AI) has advantages such as improving work performance, enhancing time efficiency, and reducing work costs, and has been widel...
Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled powerful autonomous agents capable of complex reasoni...
BACKGROUND: Artificial intelligence (AI) is transforming healthcare, but concerns about algorithmic biases and ethical challenges hinder patient accep...
Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior wo...
Graph Neural Networks (GNNs) achieve high performance across many applications but function as black-box models, limiting their use in critical doma...