Predictive artificial intelligence (AI) systems based on deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings, but can make errors in cases accurately diagnosed by clinicians and vice v...
Journal of the American College of Radiology : JACR
Jul 8, 2023
PURPOSE: The aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision sup...
This literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured over...
Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analy...
Journal of chemical information and modeling
Jun 5, 2023
Fast and accurate prospective predictions of regioselectivity can significantly reduce the time and resources spent on unproductive transformations in the pharmaceutical industry. Density functional theory (DFT) reaction modeling through transition s...
AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still ...
International journal of laboratory hematology
May 31, 2023
An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing ...
Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods s...
The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive anno...
The goal of this study was to perform a pilot study to assess user-interface of radiologists with an artificial-intelligence (AI) centric workflow for detection of intracranial hemorrhage (ICH) and cervical spine fractures (CSFX). Over 12-month perio...