AIMC Topic: Humans

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Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning.

Biosensors
Repetitive lifting tasks in occupational settings often result in shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these tasks remains a significant challenge in occupational ergonomics, partic...

Application of Wearable Insole Sensors in In-Place Running: Estimating Lower Limb Load Using Machine Learning.

Biosensors
Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practi...

KAP regarding ChatGPT among health care professionals: correspondence.

The American journal of managed care
This letter discusses previously published research that paves the way for deeper exploration of the ethical and human aspects of artificial intelligence in health care.

CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) has become a crucial research area for many applications, such as Healthcare, surveillance, etc. With the development of artificial intelligence (AI) and Internet of Things (IoT), sensor-based HAR has gained increasin...

Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications.

Sensors (Basel, Switzerland)
The state of the art in IR thermal imaging methods for applications in medical diagnostics is discussed. A review of advances in IR thermal imaging technology in the years 1960-2024 is presented. Recently used artificial intelligence (AI) methods in ...

Predicting abatacept retention using machine learning.

Arthritis research & therapy
BACKGROUND: The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improvi...

ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.

BMC oral health
OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty...

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.

BMC public health
BACKGROUND: Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting in...

Multi-branch convolutional neural network with cross-attention mechanism for emotion recognition.

Scientific reports
Research on emotion recognition is an interesting area because of its wide-ranging applications in education, marketing, and medical fields. This study proposes a multi-branch convolutional neural network model based on cross-attention mechanism (MCN...

Me vs. the machine? Subjective evaluations of human- and AI-generated advice.

Scientific reports
Artificial intelligence ("AI") has the potential to vastly improve human decision-making. In line with this, researchers have increasingly sought to understand how people view AI, often documenting skepticism and even outright aversion to these tools...