Ethics

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography.

The relationship between brain connections and non-imaging phenotypes is increasingly studied using ...

The ethics of artificial intelligence use in university libraries in Zimbabwe.

INTRODUCTION: The emergence of artificial intelligence (AI) has revolutionised higher education teac...

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunot...

The role of large language models in the peer-review process: opportunities and challenges for medical journal reviewers and editors.

The peer review process ensures the integrity of scientific research. This is particularly important...

A recurrence model for non-puerperal mastitis patients based on machine learning.

OBJECTIVE: Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non...

Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as signif...

Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings.

Intracellular electrophysiology is essential in neuroscience, cardiology, and pharmacology for study...

Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures.

BACKGROUND: To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenoc...

Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.

Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical ...

Sex-based variations in breath-holding: oxygen storage and diving response among non-divers.

Breath-hold diving performances are typically better in men than in women. However, it is still bein...

XIS-temperature: A daily spatiotemporal machine-learning model for air temperature in the contiguous United States.

The challenge of reconstructing air temperature for environmental applications is to accurately esti...

Using supervised machine learning and ICD10 to identify non-accidental trauma in pediatric trauma patients in the Maryland Health Services Cost Review Commission dataset.

BACKGROUND: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We ...

SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma.

Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying m...

Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.

Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, ...

Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach.

PURPOSE: The incidence of cancer, which is a serious public health concern, is increasing. A predict...

Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain.

OBJECTIVE: The LungFlag risk prediction model uses individualized clinical variables to identify ind...

Two-stage Non-Intrusive Load Monitoring method for multi-state loads.

The loads that have several working states cannot be accurately distinguished by the conventional No...

High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major c...

Deep learning-based encryption scheme for medical images using DCGAN and virtual planet domain.

The motivation for this article stems from the fact that medical image security is crucial for maint...

Cyber-bioethics: the new ethical discipline for digital health.

AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.

BACKGROUND: Non-muscle-invasive Bladder Cancer (NMIBC) is notorious for its high recurrence rate of ...