Staffing & Scheduling

Artificial neural networks for magnetoencephalography: a review of an emerging field.

. Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate ...

Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB using machine learning techniques.

OBJECTIVES: Training public health personnel is crucial for enhancing the capacity of public health ...

A pilot study for self-guided, active robotic training of proprioception of the upper limb in chronic stroke.

BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments ...

NMR Pure Shift Spectroscopy and Its Potential Applications in the Pharmaceutical Industry.

H nuclear magnetic resonance (NMR) spectroscopy plays an important role in the pharmaceutical indust...

Robust Uncertainty-Informed Glaucoma Classification Under Data Shift.

PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-...

Perspectives and Experiences of Nurse Managers on the Impact of Artificial Intelligence on Nursing Work Environments and Managerial Processes: A Qualitative Study.

AIM: To explore the perspectives and experiences of nurse managers regarding the impact of artificia...

We need to show AI what didn't work as well as what did.

Purification of Pharmaceuticals via Retention Time Prediction: Leveraging Graph Isomorphism Networks, Limited Data, and Transfer Learning.

The design-make-test cycle for drug discovery is highly dependent on the purification of synthesized...

Shift-invariant image classification using a bicolor shadow-casting incoherent optical system.

In this study, a shift-invariant optical pattern classification system is proposed. Optical machine ...

Addressing Workforce and Ethical Gaps in AI-Driven Mental Health Care: A Response to Higgins and Wilson.

Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for m...

Artificial Intelligence and Machine Learning Innovations to Improve Design and Representativeness in Oncology Clinical Trials.

The integration of artificial intelligence (AI) and machine learning (ML) in oncology clinical trial...

Automatic head and neck tumor segmentation through deep learning and Bayesian optimization on three-dimensional medical images.

Medical imaging constitutes critical information in the diagnostic and prognostic evaluation of pati...

SSA-sMLP: A venous thromboembolism risk prediction model using separable self-attention and spatial-shift multilayer perceptrons.

Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decisi...

Improving microsurgical suture training with automated phase recognition and skill assessment via deep learning.

Microsurgical suturing demands a high level of precision, skill, and extensive training to ensure su...

Review learning: Real world validation of privacy preserving continual learning across medical institutions.

When a deep learning model is trained sequentially on different datasets, it often forgets the knowl...

Broadening the Net: Overcoming Challenges and Embracing Novel Technologies in Lung Cancer Screening.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diag...

Automatic gesture recognition and evaluation in peg transfer tasks of laparoscopic surgery training.

BACKGROUND: Laparoscopic surgery training is gaining increasing importance. To release doctors from ...

Comparison of ChatGPT plus (version 4.0) and pretrained AI model (Orthopod) on orthopaedic in-training exam (OITE).

INTRODUCTION: Recent advancements in large language model (LLM) artificial intelligence (AI) systems...

High-Performance Computing-Based Brain Tumor Detection Using Parallel Quantum Dilated Convolutional Neural Network.

In the healthcare field, brain tumor causes irregular development of cells in the brain. One of the ...

Advances in artificial intelligence for diabetes prediction: insights from a systematic literature review.

Diabetes mellitus (DM), a prevalent metabolic disorder, has significant global health implications. ...

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.

A recent analysis of common stroke risk prediction models showed that performance differs between Bl...