Practice Management

Staffing & Scheduling

Latest AI and machine learning research in staffing & scheduling for healthcare professionals.

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Showing 1324-1344 of 6,157 articles
Effect of Flattened Structures of Molecules and Materials on Machine Learning Model Training.

A key aspect of producing accurate and reliable machine learning models for the prediction of proper...

Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications.

Brain-computer interfaces (BCI) enable direct communication between the brain and a computer or othe...

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM).

The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (A...

LMU-Net: lightweight U-shaped network for medical image segmentation.

Deep learning technology has been employed for precise medical image segmentation in recent years. H...

Using pseudo-labeling to improve performance of deep neural networks for animal identification.

Contemporary approaches for animal identification use deep learning techniques to recognize coat col...

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.

The emergence of large language models has led to the development of powerful tools such as ChatGPT ...

Artificial Intelligence Improves Novices' Bronchoscopy Performance: A Randomized Controlled Trial in a Simulated Setting.

BACKGROUND: Navigating through the bronchial tree and visualizing all bronchial segments is the init...

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks.

Modern deep neural network training is based on mini-batch stochastic gradient optimization. While u...

Robotic Medtronic Hugo™ RAS System Is Now Reality: Introduction to a New Simulation Platform for Training Residents.

The use of robotic surgery (RS) in urology has grown exponentially in the last decade, but RS traini...

Robotic locomotor training in a low-resource setting: a randomized pilot and feasibility trial.

PURPOSE: Activity-based Training (ABT) represents the current standard of neurological rehabilitatio...

Bridged adversarial training.

Adversarial robustness is considered a required property of deep neural networks. In this study, we ...

Digital transformation of mental health services.

This paper makes a case for digital mental health and provides insights into how digital technologie...

The impact of a dedicated operating room team on robotic transplant program growth and fellowship training.

INTRODUCTION: Despite considerable interest in robotic surgery, successful incorporation of robotics...

Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking.

Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and effic...

Machine learning-based radiotherapy time prediction and treatment scheduling management.

PURPOSE: The utility efficiency of medical devices is important, especially for countries such as Ch...

Advancing AI in healthcare: A comprehensive review of best practices.

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the healthcare sec...

How artificial intelligence is reshaping the autonomy and boundary work of radiologists. A qualitative study.

The application of artificial intelligence (AI) in medical practice is spreading, especially in tech...

Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans.

. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear acce...

Artificial intelligence directed development of a digital twin to measure soft tissue shift during head and neck surgery.

Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in hea...

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