Literature DB >> 7938772

Artificial intelligence in radiology: decision support systems.

C E Kahn1.   

Abstract

Computer-based systems that incorporate artificial intelligence techniques can help physicians make decisions about their patients' care. In radiology, systems have been developed to help physicians choose appropriate radiologic procedures and to formulate accurate diagnoses. These decision support systems use techniques such as rule-based reasoning, artificial neural networks, hypertext, Bayesian networks, and case-based reasoning. This article reviews these artificial intelligence techniques, describes their application in radiology, and discusses the role that decision support systems may play in radiology's future.

Entities:  

Mesh:

Year:  1994        PMID: 7938772     DOI: 10.1148/radiographics.14.4.7938772

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  18 in total

1.  Case-based explanation of non-case-based learning methods.

Authors:  R Caruana; H Kangarloo; J D Dionisio; U Sinha; D Johnson
Journal:  Proc AMIA Symp       Date:  1999

Review 2.  Modeling paradigms for medical diagnostic decision support: a survey and future directions.

Authors:  Kavishwar B Wagholikar; Vijayraghavan Sundararajan; Ashok W Deshpande
Journal:  J Med Syst       Date:  2011-10-01       Impact factor: 4.460

Review 3.  Content-based image retrieval in radiology: current status and future directions.

Authors:  Ceyhun Burak Akgül; Daniel L Rubin; Sandy Napel; Christopher F Beaulieu; Hayit Greenspan; Burak Acar
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

Review 4.  Academic radiology in the new health care delivery environment.

Authors:  Aliya Qayyum; John-Paul J Yu; Akash P Kansagra; Nathaniel von Fischer; Daniel Costa; Matthew Heller; Stamatis Kantartzis; R Scooter Plowman; Jason Itri
Journal:  Acad Radiol       Date:  2013-12       Impact factor: 3.173

5.  Voice-activated retrieval of mammography reference images.

Authors:  H A Swett; P G Mutalik; V P Neklesa; L Horvath; C Lee; J Richter; I Tocino; P R Fisher
Journal:  J Digit Imaging       Date:  1998-05       Impact factor: 4.056

Review 6.  Artificial intelligence for precision education in radiology.

Authors:  Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal:  Br J Radiol       Date:  2019-07-26       Impact factor: 3.039

7.  Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

Authors:  Samir D Mehta; Ronnie Sebro
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-03-09       Impact factor: 2.924

Review 8.  Biomedical informatics and translational medicine.

Authors:  Indra Neil Sarkar
Journal:  J Transl Med       Date:  2010-02-26       Impact factor: 5.531

9.  Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

Authors:  James J Morrison; Jason Hostetter; Kenneth Wang; Eliot L Siegel
Journal:  J Digit Imaging       Date:  2015-02       Impact factor: 4.056

10.  Workshop on using natural language processing applications for enhancing clinical decision making: an executive summary.

Authors:  Vinay M Pai; Mary Rodgers; Richard Conroy; James Luo; Ruixia Zhou; Belinda Seto
Journal:  J Am Med Inform Assoc       Date:  2013-08-06       Impact factor: 4.497

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