 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
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| MeSH Number(s) |
L01.313.500.750.300.680.900 N04.452.515.825
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| 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
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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 | | 2003 | 1 | 0 | 1 | | 2004 | 1 | 0 | 1 | | 2008 | 1 | 0 | 1 | | 2012 | 1 | 0 | 1 | | 2013 | 1 | 1 | 2 | | 2014 | 2 | 0 | 2 | | 2015 | 1 | 2 | 3 | | 2016 | 1 | 0 | 1 | | 2017 | 1 | 1 | 2 | | 2018 | 2 | 0 | 2 | | 2019 | 1 | 0 | 1 | | 2020 | 1 | 0 | 1 | | 2021 | 1 | 0 | 1 | | 2022 | 1 | 0 | 1 |
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Below are the most recent publications written about "Radiology Information Systems" by people in Profiles.
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Bridge CP, Gorman C, Pieper S, Doyle SW, Lennerz JK, Kalpathy-Cramer J, Clunie DA, Fedorov AY, Herrmann MD. Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology. J Digit Imaging. 2022 12; 35(6):1719-1737.
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Berkowitz SJ, Kwan D, Cornish TC, Silver EL, Thullner KS, Aisen A, Bui MM, Clark SD, Clunie DA, Eid M, Hartman DJ, Ho K, Leontiev A, Luviano DM, O'Toole PE, Parwani AV, Pereira NS, Rotemberg V, Vining DJ, Gaskin CM, Roth CJ, Folio LR. Interactive Multimedia Reporting Technical Considerations: HIMSS-SIIM Collaborative White Paper. J Digit Imaging. 2022 08; 35(4):817-833.
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Roth CJ, Clunie DA, Vining DJ, Berkowitz SJ, Berlin A, Bissonnette JP, Clark SD, Cornish TC, Eid M, Gaskin CM, Goel AK, Jacobs GC, Kwan D, Luviano DM, McBee MP, Miller K, Hafiz AM, Obcemea C, Parwani AV, Rotemberg V, Silver EL, Storm ES, Tcheng JE, Thullner KS, Folio LR. Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper. J Digit Imaging. 2021 06; 34(3):495-522.
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Chelala L, Hossain R, Kazerooni EA, Christensen JD, Dyer DS, White CS. Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol. 2021 06; 216(6):1411-1422.
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Callen AL, Dupont SM, Price A, Laguna B, McCoy D, Do B, Talbott J, Kohli M, Narvid J. Between Always and Never: Evaluating Uncertainty in Radiology Reports Using Natural Language Processing. J Digit Imaging. 2020 10; 33(5):1194-1201.
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Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, Bathala TK, Bittencourt LK, Booker MT, Braxton VG, Carroll PR, Casalino DD, Chang SD, Coakley FV, Dhatt R, Eberhardt SC, Foster BR, Froemming AT, F?tterer JJ, Ganeshan DM, Gertner MR, Mankowski Gettle L, Ghai S, Gupta RT, Hahn ME, Houshyar R, Kim C, Kim CK, Lall C, Margolis DJA, McRae SE, Oto A, Parsons RB, Patel NU, Pinto PA, Polascik TJ, Spilseth B, Starcevich JB, Tammisetti VS, Taneja SS, Turkbey B, Verma S, Ward JF, Warlick CA, Weinberger AR, Yu J, Zagoria RJ, Rosenkrantz AB. 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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Clark TJ, McKinney K, Jensen A, Patel NU. Risk Threshold Algorithm for Thyroid Nodule Management Demonstrates Increased Specificity and Diagnostic Accuracy as Compared With American College of Radiology Thyroid Imaging, Reporting and Data System; Society of Radiologists in Ultrasound; and American Thyroid Association Management Guidelines. Ultrasound Q. 2019 Sep; 35(3):224-227.
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Patel NU, Lind KE, Garg K, Crawford D, Werahera PN, Pokharel SS. Assessment of PI-RADS v2 categories?=?3 for diagnosis of clinically significant prostate cancer. Abdom Radiol (NY). 2019 02; 44(2):705-712.
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Au AK, Adhikari S, Slovis BH, Sachs PB, Lewiss RE. Hospital Information Technology is critical to the success of a point-of-care ultrasound program. Am J Emerg Med. 2019 03; 37(3):558-559.
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Shankar PR, Curci NE, Davenport MS. Characteristics of PI-RADS 4 lesions within the prostatic peripheral zone: a retrospective diagnostic accuracy study evaluating 170 lesions. Abdom Radiol (NY). 2018 08; 43(8):2176-2182.
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