Public Health & Policy

Work Force

Latest AI and machine learning research in work force for healthcare professionals.

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Select for better learning: identifying high-quality training data for a multimodal cyclic transformer.

. Tonic-clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epil...

Artificial Intelligence and Qualitative Analysis of Emergency Department Telemental Health Care Implementation Survey.

: Implementation of telemental health care in emergency departments (EDs) in the United States (U.S....

Manually classified dataset of leaning and standing personnel images for construction site monitoring and neural network training.

This data paper presents a manually labeled dataset of 1,214 images of personnel captured from a con...

Neurorehabilitation in spinal cord injury: Increased cortical activity through tDCS and robotic gait training.

OBJECTIVE: This study investigates the neurophysiological outcomes of combining robot-assisted gait ...

Improving machine learning models through explainable AI for predicting the level of dietary diversity among Ethiopian preschool children.

BACKGROUND: Child nutrition in Ethiopia is a significant concern, particularly for preschool-aged ch...

Class balancing diversity multimodal ensemble for Alzheimer's disease diagnosis and early detection.

Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence...

Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.

The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse ...

Application effect of rehabilitation robots in rehabilitation of limb movement disorders based on neural network algorithms.

With the continuous advancement of computer technology and sensor technology, rehabilitation robots ...

[Simulators and simulation for advanced training in orthopedic and trauma surgery : An overview].

Simulators and immersive technologies, such as virtual reality and augmented reality are becoming in...

Comparative Analysis of ChatGPT-4o and Gemini Advanced Performance on Diagnostic Radiology In-Training Exams.

Background The increasing integration of artificial intelligence (AI) in medical education and clini...

AI Chatbots for Psychological Health for Health Professionals: Scoping Review.

BACKGROUND: Health professionals face significant psychological burdens including burnout, anxiety, ...

TriDeNT : Triple deep network training for privileged knowledge distillation in histopathology.

Computational pathology models rarely utilise data that will not be available for inference. This me...

CACTUS: An open dataset and framework for automated Cardiac Assessment and Classification of Ultrasound images using deep transfer learning.

Cardiac ultrasound (US) scanning is one of the most commonly used techniques in cardiology to diagno...

: Towards Autonomous Electronic Health Record Navigation.

Clinicians spend large amounts of time on clinical documentation, and inefficiencies impact quality ...

A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.

BACKGROUND: The cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-inte...

Design and application of ISSA-BP neural network model for predicting soft tissue relaxation force.

: Accurate biomechanical modeling is crucial for enhancing the realism of virtual surgical training....

Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.

BACKGROUND: Artificial intelligence (AI), which emulates human intelligence through knowledge-based ...

Masked Deformation Modeling for Volumetric Brain MRI Self-Supervised Pre-Training.

Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated...

Pyramid Network With Quality-Aware Contrastive Loss for Retinal Image Quality Assessment.

Captured retinal images vary greatly in quality. Low-quality images increase the risk of misdiagnosi...

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes.

The ability to process visual stimuli rich with motion represents an essential skill for animal surv...

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