Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Class imbalance presents a critical challenge in machine learning applications, where conventional classifiers often exhibit systematic bias towards the majority class, significantly impairing minority-class classification accuracy. To address this issue, this paper proposes Convex Hull Delaunay Sampling (CHDS), a novel oversampling framework that combines boundary-oriented strategies with geometr...
Spinal bone metastases often lead to vertebral fractures and other skeletal events that severely affect patients' quality of life. Predicting structural failure is essential for guiding treatment and preventing complications. However, conventional assessment tools have limited predictive power, highlighting the need for computational methods capable of simulating disease progression and its mechan...
Artificial intelligence (AI) is rapidly transforming medical research and scholarly publishing, reshaping how scientific knowledge is produced, evalua...
OBJECTIVE: Detection of atherosclerotic plaque in the carotid arteries is essential for early cardiovascular risk assessment. While B-mode ultrasound ...
Medical Adaptive Machine Learning Systems (MAMLS) that continuously update their models using clinical data blur the conventional boundary between the...
Machine learning models, especially vision transformers in the domain of medical images, are highly prone to data poisoning attacks, in which a small ...
The rapid expansion of urban air mobility operations demands adaptive airspace management approaches that transcend traditional static sectorization. ...
BACKGROUND Chat Generative Pre-Trained Transformer (ChatGPT) is an advanced artificial intelligence (AI) tool that has become increasingly integrated ...
Early and accurate detection of brain tumors is clinically valuable for improving prognosis and guiding treatment. Existing deep-learning methods for ...
BACKGROUND AND OBJECTIVES: Invasive fractional flow reserve (FFR) is the clinical gold standard for assessing coronary artery stenosis, but its applic...
PURPOSE: A new method known as Lionized Remora optimization based Recurrent Neural Network (LRObRNN) is recommended to enhance the safety of medical i...
BACKGROUND: Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its scalability is constraine...
Young adults aged 18-35 years increasingly engage with conversational artificial intelligence (C-AI) in everyday contexts in which they may perceive e...
Physics-informed neural networks (PINNs) often struggle to solve stiff partial differential equations (PDEs) with moving boundaries, such as convectio...
Precise segmentation of medical images plays a crucial role in modern clinical practice, providing important foundations for the quantitative analysis...
Efficient selection of in vitro-fertilized embryos is crucial for assisted reproduction in sheep, and automated microscopic analysis can enable object...
PURPOSE: To develop and externally validate an MRI-based deep learning framework for automated 3D segmentation of neck lymph nodes (LNs) in head and n...
Accurate simulation of fluid flow in porous media is a challenging task due to the complexity of pore-space geometries and the computational cost of s...
We introduce a data-driven framework for approximating the convex set of N-representable two-electron reduced density matrices (2-RDMs). Traditional a...
Personal health large language models (PH-LLMs) have rapidly evolved from research prototypes into consumer-facing, data-linked systems that support s...