CAR - Canadian Association of Radiologists

The Canadian Association of Radiologists is the national specialty association for radiologists, dedicated to medical imaging excellence in patient care

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Suggested Reading on AI

Resources on AI, Machine Learning, and Deep Learning

General introduction to Artificial Intelligence, Machine Learning, and Deep Learning in Radiology

Implementing Machine Learning in Radiology Practice and Research1

Machine Learning for Medical Imaging2

Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference3

Big Data, Machine Learning, and Clinical Medicine4

Artificial Intelligence in Healthcare : Past, Present and Future5

Imaging Informatics - Year in Review 2017

Introductory Technical Information on AI, Machine Learning, and Deep Learning in Radiology and Healthcare

Deep Learning in Medical Image Analysis6

Machine Learning and Radiology7

A Few Useful Things to Know about Machine Learning8

Medical Image Data and Datasets in the Era of Machine Learning9

Toolkits and Libraries for Deep Learning10

Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique11

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions12

How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?13

A Roadmap Towards Machine Intelligence14

Deep Learning for Computational Biology15

Deep Learning for Healthcare: Review, Opportunities and Challenges16

Examples of Machine Learning in Radiology and Healthcare

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework17

Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems18

References

1 Kohli M, Prevedello LM, Filice RW, Geis JR. Implementing Machine Learning in Radiology Practice and Research. AJR Am J Roentgenol. 2017 Apr;208(4):754-760 https://www.ncbi.nlm.nih.gov/pubmed/28125274

2 Erickson BJ, Korfiatis P, Zeynettin A, Kline TL. Machine Learning for Medical Imaging.  Radiographics 2017 Mar-Apr;37(2):505-515   http://pubs.rsna.org/doi/full/10.1148/rg.2017160130

3 Kruskal JB, Berkowitz S, Geis JR, Kim W, Dreyer K. Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference. J Am Coll Radiol. 2017 Jun;14(6):811-817  https://www.ncbi.nlm.nih.gov/pubmed/28372961

4 Obermeyer Z, Emanuel E. Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29; 375(13): 1216–1219 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5070532/

5 Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology 2017;0: e000101. http://svn.bmj.com/content/early/2017/06/21/svn-2017-000101

6 Shen D, Wu G, Suk H-I  Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017 Jun 21; 19: 221-248. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5479722/

7 Wang S, Summers R. Machine Learning and Radiology. Med Image Anal. 2012 Jul https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3372692/

8 Domingos, P. A Few Useful Things to Know about Machine Learning. https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

9 Kohli, M.D., Summers, R.M. & Geis, J. J Digit Imaging. May 2017.  https://link.springer.com/article/10.1007%2Fs10278-017-9976-3

10 Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K. Toolkits and Libraries for Deep Learning. J Digit Imaging (2017) https://link.springer.com/article/10.1007/s10278-017-9965-6

11 Greenspan H, van Ginneken B, Summers RM. Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging (Volume: 35, Issue: 5, May 2016) http://ieeexplore.ieee.org/document/7463094/

12 Akkus, Z., Galimzianova, A., Hoogi, A. et al. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.  J Digit Imaging (2017) https://link.springer.com/article/10.1007/s10278-017-9983-4

13 Cho J, Lee K, Shin E, Choy G, Do S. How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? https://arxiv.org/pdf/1511.06348.pdf

14 Mikolov T, Joulin A, Baroni M. A Roadmap towards Machine Intelligence https://arxiv.org/pdf/1511.08130.pdf

15 Angermueller C, Parnamaa T, Parts L, Stegle O. Deep learning for computational biology. Mol Syst Biol. 2016;12(7):878. https://www.ncbi.nlm.nih.gov/pubmed/27474269

16 Miotto R, Wang F, Wang S, Jiang X, Dudley J. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2017 http://dudleylab.org/wp-content/uploads/2017/05/Deep-learning-for-healthcare-review-opportunities-and-challenges.pdf

17 Oakden-Rayner L, Carneiro G, Bessen T et al.  Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports 7, Article number: 1648 (2017) https://www.nature.com/articles/s41598-017-01931-w

18 Szlosek DA, Ferrett J. Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems EGEMS (Wash DC). 2016 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019306/