Semin Musculoskelet Radiol 2020; 24(01): 74-80
DOI: 10.1055/s-0039-3400270
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Artificial Intelligence in Radiology Residency Training

1   Section of Musculoskeletal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, Ohio
2   Department of Radiology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
Aaron F. McBride
1   Section of Musculoskeletal Imaging, Imaging Institute, Cleveland Clinic, Cleveland, Ohio
› Author Affiliations
Further Information

Publication History

Publication Date:
28 January 2020 (online)


Artificial intelligence (AI) is an emerging technology that brings a wide array of new tools to the field of radiology. AI will certainly have an impact on the day-to-day work of radiologists in the coming decades, thus training programs must prepare radiology residents adequately for their future careers. Radiology training programs should aim to give residents an understanding of the fundamentals and types of AI in radiology, the broad areas AI can be applied in radiology, how to assess AI applications in radiology, and resources available to build their knowledge in IA applications in radiology.

  • References

  • 1 Recht M, Bryan RN. Artificial intelligence: threat or boon to radiologists?. J Am Coll Radiol 2017; 14 (11) 1476-1480
  • 2 Thrall JH, Li X, Li Q. , et al. Artificial Intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. J Am Coll Radiol 2018; 15 (3 Pt B): 504-508
  • 3 Collado-Mesa F, Alvarez E, Arheart K. The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. j Am Coll Radiol 2018; 15 (12) 1753-1757
  • 4 Tajmir SH, Alkasab TK. Toward augmented radiologists: changes in radiology education in the era of machine learning and artificial intelligence. Acad Radiol 2018; 25 (06) 747-750
  • 5 Poole D, Mackworth A, Goebel R. Computational Intelligence: A Logical Approach. New York, NY: Oxford University Press; 1998
  • 6 Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 2016; 316 (22) 2353-2354
  • 7 Chockley K, Emanuel E. The end of radiology? Three threats to the future practice of radiology. J Am Coll Radiol 2016; 13 (12 Pt A): 1415-1420
  • 8 Obermeyer Z, Emanuel EJ. Predicting the future-big data, machine learning, and clinical medicine. N Engl J Med 2016; 375 (13) 1216-1219
  • 9 Lusted LB. Logical analysis in roentgen diagnosis. Radiology 1960; 74: 178-193
  • 10 He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015 . Available at: . Accessed November 20, 2019
  • 11 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems; December 3–6, 2012; Lake Tahoe, NV
  • 12 Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical diagnosis. J Appl Biomed 2013; 11 (02) 47-58
  • 13 Kao A, Poteet SR. Natural Language Processing and Text Mining. New York, NY: Springer; 2007
  • 14 Chartrand G, Cheng PM, Vorontsov E. , et al. Deep learning: a primer for radiologists. Radiographics 2017; 37 (07) 2113-2131
  • 15 Langlotz CP. RadLex: a new method for indexing online educational materials. Radiographics 2006; 26 (06) 1595-1597
  • 16 Kahn Jr CE. Artificial intelligence in radiology: decision support systems. Radiographics 1994; 14 (04) 849-861
  • 17 Massalha S, Clarkin O, Thornhill R, Wells G, Chow BJW. Decision support tools, systems, and artificial intelligence in cardiac imaging. Can J Cardiol 2018; 34 (07) 827-838
  • 18 Roshanov PS, You JJ, Dhaliwal J. , et al; CCDSS Systematic Review Team. Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review. Implement Sci 2011; 6: 88
  • 19 Jaworsky C, Pianykh O, Oglevee C. Patient feedback on waiting time displays. Am J Med Qual 2017; 32 (01) 108
  • 20 Curtis C, Liu C, Bollerman TJ, Pianykh OS. Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol 2018; 15 (09) 1310-1316
  • 21 Chen F, Taviani V, Malkiel I. , et al. Variable-density single-shot fast spin-echo MRI with deep learning reconstruction by using variational networks. Radiology 2018; 289 (02) 366-373
  • 22 Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature 2018; 555 (7697): 487-492
  • 23 Syed AB, Zoga AC. Artificial intelligence in radiology: current technology and future directions. Semin Musculoskelet Radiol 2018; 22 (05) 540-545
  • 24 Yates EJ, Yates LC, Harvey H. Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Clin Radiol 2018; 73 (09) 827-831
  • 25 Prevedello LM, Erdal BS, Ryu JL. , et al. Automated critical test findings identification and online notification system using artificial intelligence in imaging. Radiology 2017; 285 (03) 923-931
  • 26 Lee EJ, Kim YH, Kim N, Kang DW. Deep into the brain: artificial intelligence in stroke imaging. J Stroke 2017; 19 (03) 277-285
  • 27 Rajpurkar P, Irvin J, Ball RL. , et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 2018; 15 (11) e1002686
  • 28 Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 2019; 48 (02) 239-244
  • 29 Spampinato C, Palazzo S, Giordano D, Aldinucci M, Leonardi R. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal 2017; 36: 41-51
  • 30 Koopman B, Zuccon G, Wagholikar A. , et al. Automated reconciliation of radiology reports and discharge summaries. AMIA Annu Symp Proc 2015; 2015: 775-784
  • 31 Lin C, Hsu CJ, Lou YS. , et al. Artificial intelligence learning semantics via external resources for classifying diagnosis codes in discharge notes. J Med Internet Res 2017; 19 (11) e380
  • 32 Kohli M, Geis R. Ethics, artificial intelligence, and radiology. J Am Coll Radiol 2018; 15 (09) 1317-1319
  • 33 Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 2018; 15 (11) e1002683
  • 34 Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007; 31 (4–5): 198-211
  • 35 Seufert TS, Mitchell PM, Wilcox AR. , et al. An automated procedure logging system improves resident documentation compliance. Acad Emerg Med 2011; 18 (Suppl. 02) S54-S58
  • 36 Bhattacharya P, Van Stavern R, Madhavan R. Automated data mining: an innovative and efficient web-based approach to maintaining resident case logs. J Grad Med Educ 2010; 2 (04) 566-570
  • 37 Bismil R, Dudek NL, Wood TJ. In-training evaluations: developing an automated screening tool to measure report quality. Med Educ 2014; 48 (07) 724-732
  • 38 Ehrenfeld JM, McEvoy MD, Furman WR, Snyder D, Sandberg WS. Automated near-real-time clinical performance feedback for anesthesiology residents: one piece of the milestones puzzle. Anesthesiology 2014; 120 (01) 172-184
  • 39 Harari AA, Conti MB, Bokhari SA, Staib LH, Taylor CR. The role of report comparison, analysis, and discrepancy categorization in resident education. AJR Am J Roentgenol 2016; 207 (06) 1223-1231
  • 40 Chandrashekar PB, Montone K, Feldman MD, Gonzalez-Heranndez G. Automated comparison of pathology reports for on-the-job assessment of residents. AMIA Jt Summits Transl Sci Proc 2019; 2019: 495-504
  • 41 Burrows S, Gurevych I, Stein B. The eras and trends of automatic short answer grading. Int J Artif Intell Educ 2015; 25 (01) 60-117
  • 42 Zhang R, Pakhomov S, Gladding S, Aylward M, Borman-Shoap E, Melton GB. Automated assessment of medical training evaluation text. AMIA Annu Symp Proc 2012; 2012: 1459-1468
  • 43 Winkler-Schwartz A, Yilmaz R, Mirchi N. , et al. Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw Open 2019; 2 (08) e198363