AIMC Topic: Humans

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AMFormer-based framework for accident responsibility attribution: Interpretable analysis with traffic accident features.

PloS one
Accurately determining responsibility in traffic accidents is crucial for ensuring fairness in law enforcement and optimizing responsibility standards. Traditional methods predominantly rely on subjective judgments, such as eyewitness testimonies and...

Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

PloS one
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality and morbidity in China and worldwide while we are lacking in validated primary prevention model specifically for Chinese. To identify CVD high-risk individuals for early inter...

Teaching postsecondary students about the ethics of artificial intelligence: A scoping review protocol.

PloS one
The field of AI carries inherent risks such as algorithmic biases, security vulnerabilities, and ethical concerns related to privacy and data protection. Despite these risks, AI holds significant promise for social good, with applications ranging fro...

Oropouche fever outbreak in Brazil: Key factors behind the largest epidemic in history.

PloS one
Oropouche virus (OROV) is an arthropod-borne virus responsible for outbreaks of Oropouche fever (ORO) in Central and South America since the 1950s. Herein, we investigated the climatic and socioenvironmental factors contributing to the reemergence of...

Identifying key physiological and clinical factors for traumatic brain injury patient management using network analysis and machine learning.

PloS one
In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and i...

Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.

PloS one
Intracranial haemorrhage (ICH) is a crucial medical emergency that entails prompt assessment and management. Compared to conventional clinical tests, the need for computerized medical assistance for properly recognizing brain haemorrhage from compute...

AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...

A novel contrastive Dual-Branch Network (CDB-Net) for robust EEG-Based Alzheimer's disease diagnosis.

Brain research
Alzheimer's Disease (AD) is neurodegenerative disorder that causes cognitive decline, memory loss, confusion, and changes in behavior. Early and accurate detection is important for timely intervention, current diagnostic methods can be slow, expensiv...

Evaluating Large Language Models for imaging modality selection: Potential to reduce unnecessary contrast agent use and radiation exposure.

Clinical imaging
INTRODUCTION: Large Language Models (LLMs) represent a transformative leap in artificial intelligence with the potential to revolutionize radiologic decision-making. This study uniquely evaluates the performance of various LLMs from different vendors...

Understanding Physical Activity Facilitated by a Single Session of Robotic Walking for Children and Small Adults Living With Severe Mobility Impairments.

Journal of physical activity & health
BACKGROUND: Physical activity has many benefits but can be hard to achieve for people living with severe mobility impairments. Robotic walking may be an effective way for these individuals to achieve physical activity.