Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier leakage detection using dynamic contrast-enhanced MRI, without requiring pharmacokinetic models and arterial input function estimation. Materials and Met...
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...
Purpose To evaluate a sham-artificial intelligence (AI) model acting as a placebo control for a standard-AI model for diagnosis of intracranial aneurysm. Materials and Methods This retrospective crossover, blinded, multireader, multicase study was co...
IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supra...
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVE: Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of th...
Purpose To test a commercial artificial intelligence (AI) system for breast cancer detection at the BC Cancer Breast Screening Program. Materials and Methods In this retrospective study of 136 700 female individuals (mean age, 58.8 years ± 9.4 [SD]; ...
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand pa...
Purpose To develop and evaluate machine learning and deep learning-based models for automated protocoling of emergency brain MRI scans based on clinical referral text. Materials and Methods In this single-institution, retrospective study of 1953 emer...
Purpose To evaluate cancer detection and marker placement accuracy of two artificial intelligence (AI) models developed for interpretation of screening mammograms. Materials and Methods This retrospective study included data from 129 434 screening ex...
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