Radiology Information Systems
"Radiology Information Systems" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus,
MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure,
which enables searching at various levels of specificity.
Information systems, usually computer-assisted, designed to store, manipulate, and retrieve information for planning, organizing, directing, and controlling administrative activities associated with the provision and utilization of radiology services and facilities.
| Descriptor ID |
D011873
|
| MeSH Number(s) |
L01.313.500.750.300.680.900 N04.452.515.825
|
| Concept/Terms |
Radiology Information Systems- Radiology Information Systems
- Information Systems, Radiology
- Radiology Information System
- System, Radiology Information
- Systems, Radiology Information
- Systems, Radiologic Information
- Information System, Radiology
- Information Systems, Radiologic
- Radiologic Information System
- Radiologic Information Systems
- System, Radiologic Information
- Information System, Radiologic
X-Ray Information Systems- X-Ray Information Systems
- Information System, X-Ray
- Information Systems, X-Ray
- System, X-Ray Information
- Systems, X-Ray Information
- X Ray Information Systems
- X-Ray Information System
- Xray Information Systems
- Information System, Xray
- Information Systems, Xray
- System, Xray Information
- Systems, Xray Information
- Xray Information System
|
Below are MeSH descriptors whose meaning is more general than "Radiology Information Systems".
Below are MeSH descriptors whose meaning is more specific than "Radiology Information Systems".
This graph shows the total number of publications written about "Radiology Information Systems" by people in this website by year, and whether "Radiology Information Systems" was a major or minor topic of these publications.
To see the data from this visualization as text,
click here.
| Year | Major Topic | Minor Topic | Total |
|---|
| 1995 | 0 | 1 | 1 |
| 1996 | 2 | 0 | 2 |
| 1997 | 3 | 0 | 3 |
| 1998 | 3 | 0 | 3 |
| 1999 | 7 | 0 | 7 |
| 2000 | 5 | 0 | 5 |
| 2001 | 4 | 1 | 5 |
| 2002 | 7 | 0 | 7 |
| 2003 | 2 | 0 | 2 |
| 2004 | 0 | 1 | 1 |
| 2005 | 1 | 0 | 1 |
| 2006 | 1 | 1 | 2 |
| 2007 | 1 | 1 | 2 |
| 2008 | 0 | 1 | 1 |
| 2010 | 0 | 1 | 1 |
| 2011 | 1 | 1 | 2 |
| 2012 | 2 | 0 | 2 |
| 2013 | 1 | 0 | 1 |
| 2014 | 4 | 0 | 4 |
| 2015 | 2 | 0 | 2 |
| 2016 | 2 | 0 | 2 |
| 2017 | 2 | 3 | 5 |
| 2018 | 3 | 1 | 4 |
| 2019 | 3 | 0 | 3 |
| 2020 | 2 | 1 | 3 |
| 2021 | 1 | 0 | 1 |
| 2024 | 0 | 2 | 2 |
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Below are the most recent publications written about "Radiology Information Systems" by people in Profiles.
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MSKI-RADS: An MRI-based Musculoskeletal Infection Reporting and Data System for the Diagnosis of Extremity Infections. Radiology. 2024 08; 312(2):e232914.
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Commentary: The importance of utilizing ACR TI-RAD? to guide, not dictate, clinical decisions in pediatric patients. Pediatr Radiol. 2024 08; 54(9):1486-1488.
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Implementation of a Software Distribution Intervention to Improve Workload Balance in an Academic Pediatric Radiology Department. J Digit Imaging. 2021 06; 34(3):741-749.
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Response to the COVID-19 Pandemic: Practical Guide to Rapidly Deploying Home Workstations to Guarantee Radiology Services During Quarantine, Social Distancing, and Stay Home Orders. AJR Am J Roentgenol. 2020 12; 215(6):1417-1420.
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Pediatric Hepatoblastoma, Hepatocellular Carcinoma, and Other Hepatic Neoplasms: Consensus Imaging Recommendations from American College of Radiology Pediatric Liver Reporting and Data System (LI-RADS) Working Group. Radiology. 2020 09; 296(3):493-497.
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Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology. 2020 07; 296(1):76-84.
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User and system pitfalls in liver imaging with LI-RADS. J Magn Reson Imaging. 2019 12; 50(6):1673-1686.
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An update for LI-RADS: Version 2018. Why so soon after version 2017? J Magn Reson Imaging. 2019 12; 50(6):1990-1991.
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LI-RADS version 2018: What is new and what does this mean to my radiology reports? Abdom Radiol (NY). 2019 01; 44(1):41-42.
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Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting. Curr Probl Diagn Radiol. 2019 Nov - Dec; 48(6):524-530.