Patient safety / Risk Management

Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.

Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in...

Robotic Handle Prototypes for Endoscopic Endonasal Skull Base Surgery: Pre-clinical Randomised Controlled Trial of Performance and Ergonomics.

Endoscopic endonasal skull base surgery is a promising alternative to transcranial approaches. However, standard instruments lack articulation, and thus, could benefit from robotic technologies. The aim of this study was to develop an ergonomic handle for a handheld robotic instrument intended to enhance this procedure. Two different prototypes were developed based on ergonomic guidelines within t...

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images.

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide diss...

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology.

Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity ...

Formulation and physicochemical stability of oil-in-water nanoemulsion loaded with α-terpineol as flavor oil using Quillaja saponins as natural emulsifier.

Alpha-terpineol (α-TOH) is a promising monoterpenoid detaining several biological activities. However, as a volatile molecule, the incorporation of α-...

Deep Learning Based on Hierarchical Self-Attention for Finance Distress Prediction Incorporating Text.

To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, dig...

Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.

The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is i...

Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.

Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for m...

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19.

This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduat...

Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review.

A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the ad...

Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM).

PURPOSE: Our purposes were (1) to explore the methodologic quality of the studies on the deep learning in knee imaging with CLAIM criterion and (2) to...

Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know.

There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has...

Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol.

INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in ...

Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have rec...

The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies.

This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to...

Pseudo-pneumatosis of the gastrointestinal tract: its incidence and the accuracy of a checklist supported by artificial intelligence (AI) techniques to reduce the misinterpretation of pneumatosis.

PURPOSE: To assess the incidence of erroneous diagnosis of pneumatosis (pseudo-pneumatosis) in patients who underwent an emergency abdominal CT and to...

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal.

Background and purpose - Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics...

Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards.

There have been many efforts in the last decade in the health informatics community to develop systems that can automatically recognize and predict di...

Q-CHAT-NAO: A robotic approach to autism screening in toddlers.

The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results w...

The T.R.U.E. Checklist for Identifying Impactful Artificial Intelligence-Based Findings in Nuclear Medicine: Is It True? Is It Reproducible? Is It Useful? Is It Explainable?