Identifying and Reporting Dependent Adult abuse

Safety improvement requires data: the case for automation and artificial intelligence during incident reporting.

The reporting of incidents has a long association with safety in healthcare and anaesthesia, yet man...

An artificial intelligence boost to MRI lumbar spine reporting.

A systematic review of deep learning-based spinal bone lesion detection in medical images.

Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to agg...

Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits.

INTRODUCTION: Radiology plays an integral role in fracture detection in the emergency department (ED...

Perceptions and detection of AI use in manuscript preparation for academic journals.

The rapid advances in Generative AI tools have produced both excitement and worry about how AI will ...

AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review.

BACKGROUND: The reporting of adverse events (AEs) relating to medical devices is a long-standing are...

Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge.

Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk ...

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study.

PURPOSE: To develop a deep learning (DL) model for differentiating between benign and malignant ovar...

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system.

PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyr...

ChatGPT and assistive AI in structured radiology reporting: A systematic review.

INTRODUCTION: The rise of transformer-based large language models (LLMs), such as ChatGPT, has captu...

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to conside...

Predicting and refining acid modifications of biochar based on machine learning and bibliometric analysis: Specific surface area, average pore size, and total pore volume.

Acid-modified biochar is a modified biochar material with convenient preparation, high specific surf...

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs.

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types o...

A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.

BACKGROUND:  Predicting 30-day hospital readmissions is crucial for improving patient outcomes, opti...

Quality of science journalism in the age of Artificial Intelligence explored with a mixed methodology.

Science journalists, traditionally, play a key role in delivering science information to a wider aud...

Systematic review of machine learning models in predicting the risk of bleed/grade of esophageal varices in patients with liver cirrhosis: A comprehensive methodological analysis.

Esophageal varices (EV) in liver cirrhosis carry high mortality risks. Traditional endoscopy, which ...

Tackling the small data problem in medical image classification with artificial intelligence: a systematic review.

Though medical imaging has seen a growing interest in AI research, training models require a large a...

Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI).

INTRODUCTION: Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reportin...

Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.

The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summar...

Radial Undersampled MRI Reconstruction Using Deep Learning With Mutual Constraints Between Real and Imaginary Components of K-Space.

The deep learning method is an efficient solution for improving the quality of undersampled magnetic...

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed...