Work Force

Artificial Intelligence and Radiologist Burnout.

IMPORTANCE: Understanding the association of artificial intelligence (AI) with physician burnout is ...

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily...

AI for biofabrication.

Biofabrication is an advanced technology that holds great promise for constructing highly biomimetic...

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis...

Enhancing Efficiency with an AI-Augmented Clinician in Neurology.

Integrating artificial intelligence (AI) technologies into neurology promises increased patient acce...

Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein str...

A deep learning algorithm that aids visualization of femoral neck fractures and improves physician training.

PURPOSE: Missed fractures are the most common radiologic error in clinical practice, and erroneous c...

Effective deep-learning brain MRI super resolution using simulated training data.

BACKGROUND: In the field of medical imaging, high-resolution (HR) magnetic resonance imaging (MRI) i...

Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction.

The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at...

Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach.

Quantifying individualized rehabilitation responses and optimizing therapy for each person is challe...

From pre-training to fine-tuning: An in-depth analysis of Large Language Models in the biomedical domain.

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Larg...

Pre-training strategy for antiviral drug screening with low-data graph neural network: A case study in HIV-1 K103N reverse transcriptase.

Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in dr...

Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.

Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performa...

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oe...

Enhancing Data Science and Genomics Capacity of a Historically Black Medical College Through Interdisciplinary Training and Research Collaborations.

As data grows exponentially across diverse fields, effectively leveraging big data has become increa...

Mastery Learning Guided by Artificial Intelligence Is Superior to Directed Self-Regulated Learning in Flexible Bronchoscopy Training: An RCT.

INTRODUCTION: Simulation-based training has proven effective for learning flexible bronchoscopy. How...

Artificial intelligence for training and reporting infection prevention measures in critical wards.

Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls ahead.

Expansion of artificial intelligence (AI) in the field of medicine is changing the paradigm of clini...

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Depression in adolescents is a serious mental health condition that can affect their emotional and s...

Less is More: Selective reduction of CT data for self-supervised pre-training of deep learning models with contrastive learning improves downstream classification performance.

BACKGROUND: Self-supervised pre-training of deep learning models with contrastive learning is a wide...

Development of HepatIA: A computed tomography annotation platform and database for artificial intelligence training in hepatocellular carcinoma detection at a Brazilian tertiary teaching hospital.

BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent tumor with high mortality rates. Computed ...