Literature DB >> 31322909

Artificial intelligence for precision education in radiology.

Michael Tran Duong1,2, Andreas M Rauschecker2,3, Jeffrey D Rudie2,3, Po-Hao Chen4, Tessa S Cook2, R Nick Bryan2,5, Suyash Mohan2,6.   

Abstract

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.

Mesh:

Year:  2019        PMID: 31322909      PMCID: PMC6849670          DOI: 10.1259/bjr.20190389

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  59 in total

1.  Integration of a multimedia teaching and reference database in a PACS environment.

Authors:  Antoine Rosset; Osman Ratib; Antoine Geissbuhler; Jean-Paul Vallée
Journal:  Radiographics       Date:  2002 Nov-Dec       Impact factor: 5.333

Review 2.  Metrics for Radiologists in the Era of Value-based Health Care Delivery.

Authors:  Ammar Sarwar; Giles Boland; Annamarie Monks; Jonathan B Kruskal
Journal:  Radiographics       Date:  2015-04-03       Impact factor: 5.333

3.  A new initiative on precision medicine.

Authors:  Francis S Collins; Harold Varmus
Journal:  N Engl J Med       Date:  2015-01-30       Impact factor: 91.245

4.  Attending Radiologist Variability and Its Effect on Radiology Resident Discrepancy Rates.

Authors:  Joseph C Wildenberg; Po-Hao Chen; Mary H Scanlon; Tessa S Cook
Journal:  Acad Radiol       Date:  2017-01-24       Impact factor: 3.173

5.  Resident Case Volume Correlates with Clinical Performance: Finding the Sweet Spot.

Authors:  Vikas Agarwal; Gregory M Bump; Matthew T Heller; Ling-Wan Chen; Barton F Branstetter; Nikhil B Amesur; Marion A Hughes
Journal:  Acad Radiol       Date:  2018-08-04       Impact factor: 3.173

6.  Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

Authors:  Mindy K Ross; Jinsung Yoon; Auke van der Schaar; Mihaela van der Schaar
Journal:  Ann Am Thorac Soc       Date:  2018-01

7.  Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Authors:  Muhammad Febrian Rachmadi; Maria Del C Valdés-Hernández; Maria Leonora Fatimah Agan; Carol Di Perri; Taku Komura
Journal:  Comput Med Imaging Graph       Date:  2018-02-17       Impact factor: 4.790

8.  Influence of computer-aided detection on performance of screening mammography.

Authors:  Joshua J Fenton; Stephen H Taplin; Patricia A Carney; Linn Abraham; Edward A Sickles; Carl D'Orsi; Eric A Berns; Gary Cutter; R Edward Hendrick; William E Barlow; Joann G Elmore
Journal:  N Engl J Med       Date:  2007-04-05       Impact factor: 91.245

9.  Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.

Authors:  Zhiqiang Lao; Dinggang Shen; Dengfeng Liu; Abbas F Jawad; Elias R Melhem; Lenore J Launer; R Nick Bryan; Christos Davatzikos
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

10.  Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning.

Authors:  Panagiotis Korfiatis; Timothy L Kline; Bradley J Erickson
Journal:  Tomography       Date:  2016-12
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  16 in total

1.  Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Authors:  Y W Lui; P D Chang; G Zaharchuk; D P Barboriak; A E Flanders; M Wintermark; C P Hess; C G Filippi
Journal:  AJNR Am J Neuroradiol       Date:  2020-07-30       Impact factor: 3.825

2.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

Review 3.  Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.

Authors:  Issam El Naqa; Masoom A Haider; Maryellen L Giger; Randall K Ten Haken
Journal:  Br J Radiol       Date:  2020-02-01       Impact factor: 3.039

4.  Machine learning and clinical neurophysiology.

Authors:  Julian Ray; Lokesh Wijesekera; Silvia Cirstea
Journal:  J Neurol       Date:  2022-07-30       Impact factor: 6.682

5.  Individualized and generalized models for predicting observer performance on liver metastasis detection using CT.

Authors:  Parvathy Sudhir Pillai; David R Holmes; Rickey Carter; Akitoshi Inoue; David A Cook; Ron Karwoski; Jeff L Fidler; Joel G Fletcher; Shuai Leng; Lifeng Yu; Cynthia H McCollough; Scott S Hsieh
Journal:  J Med Imaging (Bellingham)       Date:  2022-09-13

Review 6.  Artificial intelligence in paediatric radiology: Future opportunities.

Authors:  Natasha Davendralingam; Neil J Sebire; Owen J Arthurs; Susan C Shelmerdine
Journal:  Br J Radiol       Date:  2020-09-17       Impact factor: 3.039

7.  Designing a Clinical Education Tracking System: An Innovative Approach.

Authors:  Abdullah Alismail; Braden Michael Tabisula; David López
Journal:  Adv Med Educ Pract       Date:  2021-05-26

8.  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

Review 9.  How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods.

Authors:  Kamil Zeleňák; Antonín Krajina; Lukas Meyer; Jens Fiehler; Daniel Behme; Deniz Bulja; Jildaz Caroff; Amar Ajay Chotai; Valerio Da Ros; Jean-Christophe Gentric; Jeremy Hofmeister; Omar Kass-Hout; Özcan Kocatürk; Jeremy Lynch; Ernesto Pearson; Ivan Vukasinovic
Journal:  Life (Basel)       Date:  2021-05-27

10.  Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Authors:  Nita G Valikodath; Emily Cole; Daniel S W Ting; J Peter Campbell; Louis R Pasquale; Michael F Chiang; R V Paul Chan
Journal:  Transl Vis Sci Technol       Date:  2021-06-01       Impact factor: 3.283

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