AIMC Topic: Humans

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Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom.

BMC medical education
BACKGROUND: With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI...

A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...

Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

BMC medical informatics and decision making
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to cre...

Machine learning-based characterization of stemness features and construction of a stemness subtype classifier for bladder cancer.

BMC cancer
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. Th...

AI-enhanced guidance demonstrated improvement in novices' Apical-4-chamber and Apical-5-chamber views.

BMC medical education
INTRODUCTION: Artificial Intelligence (AI) modules might simplify the complexities of cardiac ultrasound (US) training by offering real-time, step-by-step guidance on probe manipulation for high-quality diagnostic imaging. This study investigates rea...

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Scientific reports
Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high radiation d...

Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.

Scientific reports
Worldwide, coronary heart disease (CHD) is a leading cause of mortality, and its early prediction remains a critical challenge in clinical data analysis. Machine learning (ML) offers valuable diagnostic support by leveraging healthcare data to enhanc...

Identification of cancerous tissues based on residual neural network.

Scientific reports
The identification of cancerous tissues remains challenging due to the complexity of experimental methods and low identification accuracy rates. Therefore, this paper proposes a rapid identification method. We introduce a new theoretical transmission...

Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.

Scientific reports
With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote healt...

VMKLA-UNet: vision Mamba with KAN linear attention U-Net.

Scientific reports
In the domain of medical image segmentation, while convolutional neural networks (CNNs) and Transformer-based architectures have attained notable success, they continue to face substantial challenges. CNNs are often limited in their ability to captur...