Public Health & Policy

Work Force

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

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Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?

In recent decades, technological shifts within the healthcare sector have significantly transformed ...

Artificial intelligence and natural language processing for improved telemedicine: Before, during and after remote consultation.

The rapid evolution of telemedicine has revealed significant documentation and workflow challenges. ...

Machine learning tools match physician accuracy in multilingual text annotation.

In the medical field, text annotation involves categorizing clinical and biomedical texts with speci...

Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases.

Metagenomics, particularly genome-resolved metagenomics, have significantly deepened our understandi...

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging whil...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT exami...

Generative Artificial Intelligence in Medical Education-Policies and Training at US Osteopathic Medical Schools: Descriptive Cross-Sectional Survey.

BACKGROUND: Interest has recently increased in generative artificial intelligence (GenAI), a subset ...

The good, the bad, and the ugly: Ethical considerations regarding artificial intelligence assistance in administrative physician tasks.

Artificial intelligence is a powerful tool that can potentially transform the diagnostic, therapeuti...

The physiological responses to volume-matched high-intensity functional training protocols with varied time domains.

BACKGROUND: High-intensity functional training (HIFT) is typically performed with minimal or no rest...

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their function...

Can muscle synergies shed light on the mechanisms underlying motor gains in response to robot-assisted gait training in children with cerebral palsy?

BACKGROUND: Children with cerebral palsy (CP) often experience gait impairments. Robot-assisted gait...

Scaling Graph Neural Networks to Large Proteins.

Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting hi...

Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions.

Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenge...

Breast cancer classification based on hybrid CNN with LSTM model.

Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. ...

Education and Training Assessment and Artificial Intelligence. A Pragmatic Guide for Educators.

The emergence of ChatGPT and similar new Generative AI tools has created concern about the validity ...

Cost-efficient training of hyperspectral deep learning models for the detection of contaminating grains in bulk oats by fluorescent tagging.

Computer vision based on instance segmentation deep learning models offers great potential for autom...

Error fields: personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the ne...

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training.

Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous conc...

DC²T: Disentanglement-Guided Consolidation and Consistency Training for Semi-Supervised Cross-Site Continual Segmentation.

Continual Learning (CL) is recognized to be a storage-efficient and privacy-protecting approach for ...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it mu...

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