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Mashup Score: 9Energy and Greenhouse Gas Emission Savings Associated with Implementation of an Abbreviated Cardiac MRI Protocol - 2 day(s) ago
Supplemental material is available for this article. See also the article by Lenkinski and Rofsky in this issue. See also the article by McKee et al in this issue.
Source: pubs.rsna.orgCategories: General Medicine News, Cardiologists1Tweet
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Mashup Score: 3
Climate change adversely affects the well-being of humans and the entire planet. A planetary health framework recognizes that sustaining a healthy planet is essential to achieving individual, community, and global health. Radiology contributes to the climate crisis by generating greenhouse gas (GHG) emissions during the production and use of medical imaging equipment and supplies. To promote planetary health, strategies that mitigate and adapt to climate change in radiology are needed. Mitigation strategies to reduce GHG emissions include switching to renewable energy sources, refurbishing rather than replacing imaging scanners, and powering down unused scanners. Radiology departments must also build resiliency to the now unavoidable impacts of the climate crisis. Adaptation strategies include education, upgrading building infrastructure, and developing departmental sustainability dashboards to track progress in achieving sustainability goals. Shifting practices to catalyze these neces
Source: pubs.rsna.orgCategories: General Medicine News, NeurologyTweet
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Mashup Score: 107Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy - 11 day(s) ago
Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories
Source: pubs.rsna.orgCategories: General Medicine News, Expert PicksTweet
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Mashup Score: 1Radiology: Artificial Intelligence - 21 day(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? An end-to-end deep learning pipeline provides standardized segmentation for patients with single ventricle physiology (Yao and St. Clair et al). A CNN trained on data from three brain cancer datasets accurately classifies MR image sequences (Mahmutoglu et al). See the latest information about Radiology: Artificial Intelligen
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 2Assessment of Claimant, Clinical, and Financial Characteristics of Teleradiology Medical Malpractice Cases - 23 day(s) ago
Background The increasing use of teleradiology has been accompanied by concerns relating to risk management and patient safety. Purpose To compare characteristics of teleradiology and nonteleradiology radiology malpractice cases and identify contributing factors underlying these cases. Materials and Methods In this retrospective analysis, a national database of medical malpractice cases was queried to identify cases involving telemedicine that closed between January 2010 and March 2022. Teleradiology malpractice cases were identified based on manual review of cases in which telemedicine was coded as one of the contributing factors. These cases were compared with nonteleradiology cases that closed during the same time period in which radiology had been determined to be the primary responsible clinical service. Claimant, clinical, and financial characteristics of the cases were recorded, and continuous or categorical data were compared using the Wilcoxon rank-sum test or Fisher exact tes
Source: pubs.rsna.orgCategories: General Medicine News, Hem/OncsTweet
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Mashup Score: 2Epicardial Space: Comprehensive Anatomy and Spectrum of Disease - 1 month(s) ago
The epicardial space (ES) is the anatomic region located between the myocardium and the pericardium. This space includes the visceral pericardium and the epicardial fat that contains the epicardial coronary arteries, cardiac veins, lymphatic channels, and nerves. The epicardial fat represents the main component of the ES. This fat deposit has been a focus of research in recent years owing to its properties and relationship with coronary atherosclerotic plaque and atrial fibrillation. Although this region is sometimes forgotten, a broad spectrum of lesions can be found in the ES and can be divided into neoplastic and nonneoplastic categories. Epicardial neoplastic lesions include lipoma, paraganglioma, metastases, angiosarcoma, and lymphoma. Epicardial nonneoplastic lesions encompass inflammatory infiltrative disorders, such as immunoglobulin G4–related disease and Erdheim-Chester disease, along with hydatidosis, abscesses, coronary abnormalities, pseudoaneurysms, hematoma, lipomatosis,
Source: pubs.rsna.orgCategories: General Medicine News, CardiologistsTweet
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Mashup Score: 2
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? V olume: 291 Issue: 1 pp. 53-59 Volume: 299 Issue: 1 pp. 120-121 Volume: 297 Issue: 3 pp.
Source: pubs.rsna.orgCategories: General Medicine News, CardiologistsTweet
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Mashup Score: 5Celebrating the Journal’s First 5 Years - 1 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? 1. Kahn CE Jr.. Radiol Artif Intell 2023;5(6):e230418. Link, Google Scholar 2. Brady AP, Allen B, Chong J, et al.. Radiol Artif Intell 2024;6(1):e230513. Link, Google Scholar 3. Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH.. Radiol Artif Intell 2024:6(2):e230327. Link, Google
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 1Radiology: Artificial Intelligence - 2 month(s) ago
Can’t sign in? Forgot your password? If the address matches an existing account you will receive an email with instructions to reset your password. Can’t sign in? Forgot your username? An end-to-end deep learning pipeline provides standardized segmentation for patients with single ventricle physiology (Yao and St. Clair et al). A CNN trained on data from three brain cancer datasets accurately classifies MR image sequences (Mahmutoglu et al). See the latest information about Radiology: Artificial Intelligen
Source: pubs.rsna.orgCategories: General Medicine News, Future of MedicineTweet
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Mashup Score: 77Coronary Artery Calcium Score Predicts Major Adverse Cardiovascular Events in Stable Chest Pain - 2 month(s) ago
Background Coronary artery calcium (CAC) has prognostic value for major adverse cardiovascular events (MACE) in asymptomatic individuals, whereas its role in symptomatic patients is less clear. Purpose To assess the prognostic value of CAC scoring for MACE in participants with stable chest pain initially referred for invasive coronary angiography (ICA). Materials and Methods This prespecified subgroup analysis from the Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease (DISCHARGE) trial, conducted between October 2015 and April 2019 across 26 centers in 16 countries, focused on adult patients with stable chest pain referred for ICA. Participants were randomly assigned to undergo either ICA or coronary CT. CAC scores from noncontrast CT scans were categorized into low, intermediate, and high groups based on scores of 0, 1–399, and 400 or higher, respectively. The end point of the study was the occurrence of MACE (myocardial
Source: pubs.rsna.orgCategories: General Medicine News, Cardiologists1Tweet
RT @KateHanneman: Happy to share our latest @radiology_rsna publication 😊 https://t.co/ZhkEj8xlsr Implementation of an abbreviated #CMR pro…