Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Optimal transport (OT) has proven highly successful in various machine learning tasks, primarily by measuring distributional differences. As a distance-driven method, OT heavily relies on the underlying distance structure of the sample space but lacks the ability to incorporate label-related information. Consequently, it often underperforms and suffers from significant degradation in real-world sc...
Accurate detection of blood cells-red blood cells, white blood cells, and platelets-is essential for diagnosing hematological disorders such as anemia and leukemia. However, traditional approaches face critical limitations: manual microscopy is labor-intensive and subjective, automated analyzers fail to resolve complex morphologies in dense cellular scenes, and most deep learning models prioritize...
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as the most common chronic liver disorder worldwide. However, most of the...
The rising prevalence of chronic diseases demands a more effective healthcare management approach to overcome the constraints of existing health monit...
Artificially intelligent (AI) chatbots are increasingly used for mental health support, yet their safety guidelines and ethical structures remain uncl...
Existing medical polyp segmentation networks predominantly rely on hierarchical feature representations to improve boundary delineation. However, we i...
Digital infrastructures such as platforms, algorithms, and artificial intelligence (AI) are rapidly reshaping the conditions under which nursing care ...
Accurate and rapid mapping of burned areas is critical for understanding the impacts of forest fires on ecosystems, the carbon cycle, and post-fire re...
BACKGROUND: Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are 2 of the leading causes of vision loss worldwide. As population a...
Multi-modal models that fuse neuroimaging with clinical assessment data represent the current state of the art for automated Alzheimer's disease detec...
The combinations of Convolutional Neural Networks (CNNs) and Transformer have shown promising results in many medical image segmentation tasks. Howeve...
BACKGROUND: The rapid advancement of Large Language Models (LLMs) presents unprecedented opportunities for healthcare education and professional crede...
BACKGROUND: Effective expatriate management has become crucial in the health care sector, driven by the growing number of globally mobile professional...
The pursuit of nanoscale light manipulation represents a fundamental challenge in nanophotonics, where overcoming the diffraction limit is essential f...
Skin cancer is among the most common and dangerous forms of cancer worldwide. The earlier stage lesions, if not diagnosed on time, transform into canc...
Class imbalance presents a critical challenge in machine learning applications, where conventional classifiers often exhibit systematic bias towards t...
Spinal bone metastases often lead to vertebral fractures and other skeletal events that severely affect patients' quality of life. Predicting structur...
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...