Literature DB >> 19234247

Biases in radiologic reasoning.

Richard B Gunderman1.   

Abstract

OBJECTIVE: The purpose of this article is to outline common biases in medical reasoning that contribute to avoidable errors in diagnostic and therapeutic decision making.
CONCLUSION: By recognizing and understanding common biases in medical reasoning, we can more effectively counteract them.

Mesh:

Year:  2009        PMID: 19234247     DOI: 10.2214/AJR.08.1220

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  14 in total

1.  Reporting instructions significantly impact false positive rates when reading chest radiographs.

Authors:  John W Robinson; Patrick C Brennan; Claudia Mello-Thoms; Sarah J Lewis
Journal:  Eur Radiol       Date:  2016-01-15       Impact factor: 5.315

2.  The new pediatric radiologist.

Authors:  Sarah Sarvis Milla; Ryan W Arnold
Journal:  Pediatr Radiol       Date:  2010-04

3.  Diagnostic image quality in gynaecological ultrasound: Who should measure it, what should we measure and how?

Authors:  Peter Cantin; Karen Knapp
Journal:  Ultrasound       Date:  2013-11-28

Review 4.  Bias in Radiology: The How and Why of Misses and Misinterpretations.

Authors:  Lindsay P Busby; Jesse L Courtier; Christine M Glastonbury
Journal:  Radiographics       Date:  2017-12-01       Impact factor: 5.333

5.  Non-contrast MR angiography using three-dimensional balanced steady-state free-precession imaging for evaluation of stenosis in the celiac trunk and superior mesenteric artery: a preliminary comparative study with computed tomography angiography.

Authors:  Patricia P Cardia; Thiago J Penachim; Adilson Prando; Ulysses S Torres; Giuseppe D'Ippólito
Journal:  Br J Radiol       Date:  2017-06-07       Impact factor: 3.039

6.  Unbiased review of digital diagnostic images in practice: informatics prototype and pilot study.

Authors:  Anthony F Fotenos; Nabile M Safdar; Paul G Nagy; Reuben Mezrich; Jonathan S Lewin
Journal:  Acad Radiol       Date:  2012-10-26       Impact factor: 3.173

7.  Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study.

Authors:  Jeffrey D Rudie; Andreas M Rauschecker; Long Xie; Jiancong Wang; Michael Tran Duong; Emmanuel J Botzolakis; Asha Kovalovich; John M Egan; Tessa Cook; R Nick Bryan; Ilya M Nasrallah; Suyash Mohan; James C Gee
Journal:  Radiol Artif Intell       Date:  2020-09-23

8.  Does anthropomorphic model design in ex vivo studies affect diagnostic accuracy for dental root fracture using CBCT?

Authors:  Fedil Andraws Yalda; Rosalyn J Clarkson; Jonathan Davies; Peter G J Rout; Anita Sengupta; Keith Horner
Journal:  Dentomaxillofac Radiol       Date:  2020-06-09       Impact factor: 2.419

9.  Image perception and interpretation of abnormalities; can we believe our eyes? Can we do something about it?

Authors:  Durr-E- Sabih; Ayan Sabih; Quratulain Sabih; Ali N Khan
Journal:  Insights Imaging       Date:  2010-10-24

10.  Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Authors:  Jeffrey D Rudie; Jeffrey Duda; Michael Tran Duong; Po-Hao Chen; Long Xie; Robert Kurtz; Jeffrey B Ware; Joshua Choi; Raghav R Mattay; Emmanuel J Botzolakis; James C Gee; R Nick Bryan; Tessa S Cook; Suyash Mohan; Ilya M Nasrallah; Andreas M Rauschecker
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.903

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