Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
The rapid growth of artificial-intelligence computing demands high-bandwidth and energy-efficient data-center interconnects. Although self-homodyne coherent (SHC) transmission reduces digital signal-processing complexity, its practical deployment is limited by ultrafast state-of-polarization (SOP) fluctuations that destabilize coherent reception. Here we show an ultrafast adaptive polarization con...
The rapid deployment and usage of 5G and IoT networks in smart cities present significant challenges for cybersecurity, particularly regarding energy efficiency and attack detection. Existing intrusion detection systems often fail to balance high detection accuracy with low energy consumption, especially in resource-constrained environments. This research paper proposes an ontology-driven novel co...
Consumer artificial intelligence chatbots are now accessed by hundreds of millions of users seeking health information, yet systematic evaluation of t...
BACKGROUND: Artificial intelligence (AI) is rapidly transforming surgical research and medical publishing by changing how clinicians discover, evaluat...
Medical image segmentation requires balancing accuracy, computational efficiency, and uncertainty quantification for potential clinical deployment. Tr...
Endometrial carcinoma ranks among the most common malignancies of the female reproductive system. Accurate early-stage staging is essential for devisi...
BACKGROUND: Anxiety is one of the most prevalent mental health concerns among college students worldwide, yet traditional assessment methods relying o...
Optimal transport (OT) has proven highly successful in various machine learning tasks, primarily by measuring distributional differences. As a distanc...
Accurate detection of blood cells-red blood cells, white blood cells, and platelets-is essential for diagnosing hematological disorders such as anemia...
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...