Practice Management

Staffing & Scheduling

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

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Showing 3445-3465 of 6,203 articles
ChatIOS: Improving automatic 3-dimensional tooth segmentation via GPT-4V and multimodal pre-training.

OBJECTIVES: This study aims to propose a framework that integrates GPT-4V, a recent advanced version...

Robotics in endodontics: A comprehensive scoping review.

BACKGROUND: Robotics in endodontics enhances precision, efficiency, and treatment success through AI...

GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.

BACKGROUND AND OBJECTIVE: Research in the cross-modal medical image translation domain has been very...

S-Net: A novel shallow network for enhanced detail retention in medical image segmentation.

BACKGROUND AND OBJECTIVE: In recent years, deep U-shaped network architectures have been widely appl...

Artificial intelligence (AI) use for personal protective equipment training, remediation, and education in health care.

BACKGROUND: Personal protective equipment (PPE) is a first-line transmission-based precaution for re...

Artificial intelligence applications in rare and intractable diseases: Advances, challenges, and future directions.

Rare and intractable diseases affect an estimated 3.5% to 5.9% of the global population but remain l...

Enhanced neuroplasticity and gait recovery in stroke patients: a comparative analysis of active and passive robotic training modes.

BACKGROUND: Stroke is a leading cause of long-term disability, with lower limb dysfunction being a c...

Predicting and Understanding Work Functions of Double Transition Metal MXenes via Interpretable Machine Learning Methods.

In this study, we employed interpretable machine learning models to predict and understand the work ...

Machine learning for predicting retention times of chiral analytes chromatographically separated by CMPA technique.

Chiral mobile phase additive (CMPA) technique is an attractive method for chromatographic enantiosep...

Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (S...

Evaluating machine- and deep learning approaches for artifact detection in infant EEG: classifier performance, certainty, and training size effects.

Electroencephalography (EEG) is essential for studying infant brain activity but is highly susceptib...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscop...

Towards precision agriculture tea leaf disease detection using CNNs and image processing.

In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task ...

Unsupervised post-training learning in spiking neural networks.

The human brain is a dynamic system that is constantly learning. It employs a combination of various...

Machine learning-assisted literature screening for a medication-use process-related systematic review.

PURPOSE: This article summarizes a novel methodology of applying machine learning (ML) algorithms tr...

[Development and validation of risk assessment models for abnormal lung function in coal workers based on machine learning].

To analyze the factors influencing the lung function of coal miners, identify the optimal combinati...

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