Public Health & Policy

Work Force

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

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AdaSAM: Boosting sharpness-aware minimization with adaptive learning rate and momentum for training deep neural networks.

Sharpness aware minimization (SAM) optimizer has been extensively explored as it can generalize bett...

Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym.

A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneousl...

The value of standards for health datasets in artificial intelligence-based applications.

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosi...

A robot-aided visuomotor wrist training induces motor and proprioceptive learning that transfers to the untrained ipsilateral elbow.

BACKGROUND: Learning of a visuomotor task not only leads to changes in motor performance but also im...

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Effective solutions to conserve biodiversity require accurate community- and species-level informati...

Using natural language processing to increase prediction and reduce subgroup differences in personnel selection decisions.

The purpose of this research is to demonstrate how using natural language processing (NLP) on narrat...

Health Care Trainees' and Professionals' Perceptions of ChatGPT in Improving Medical Knowledge Training: Rapid Survey Study.

BACKGROUND: ChatGPT is a powerful pretrained large language model. It has both demonstrated potentia...

Research on a New Rehabilitation Robot for Balance Disorders.

The treatment of patients with balance disorders is an urgent problem to be solved by the medical co...

Perception of Robotics and Navigation by Spine Fellows and Early Attendings: The Impact of These Technologies on Their Training and Practice.

BACKGROUND: There is scant data on the role that robotics and navigation play in spine surgery train...

The role of robot-assisted training on rehabilitation outcomes in Parkinson's disease: a systematic review and meta-analysis.

PURPOSE: The study aims to assess the efficacy of robot-assisted rehabilitation training on upper an...

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

BACKGROUND: Institutes of dermatopathology are faced with considerable challenges including a contin...

Conserve the open media ecosystem! Legal and ethical considerations when using online repositories for AI training in ecological research.

Ecological researchers who train Artificial Intelligence models using digital media have to be cogni...

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and di...

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population.

INTRODUCTION: Artificial intelligence has real potential for early identification of ocular diseases...

A self-training algorithm based on the two-stage data editing method with mass-based dissimilarity.

A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number...

Addressing the challenges of AI-based telemedicine: Best practices and lessons learned.

Telemedicine is the use of technology to provide healthcare services and information remotely, witho...

Design and Validation of a Soft Robotic Simulator for Transseptal Puncture Training.

OBJECTIVE: Transseptal puncture (TP) is the technique used to access the left atrium of the heart fr...

Self-supervised depth super-resolution with contrastive multiview pre-training.

Many low-level vision tasks, including guided depth super-resolution (GDSR), struggle with the issue...

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