SpringerLink
Log in

Are the Pilots Onboard? Equip** Radiologists for Clinical Implementation of AI

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

The incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additionally, radiology systems should be designed to avoid automation bias and the potential decline in radiologist performance. Designed solutions should cater to every level of expertise so that patient care can be enhanced and risks reduced. Ultimately, the radiology community must provide education so that radiologists can learn about algorithms, their inputs and outputs, and potential ways they may fail. This manuscript will present suggestions on how to train radiologists to use these new digital systems, how to detect AI errors, and how to maintain underlying diagnostic competency when the algorithm fails.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Doucette JN. View from the cockpit: what the airline industry can teach us about patient safety. Nursing2021 2006;36:50-53

    Google Scholar 

  2. Hudson P. Applying the lessons of high risk industries to health care. BMJ Quality & Safety 2003;12:i7-i12

    Article  Google Scholar 

  3. Gawande A. The checklist. 2007

  4. Clay-Williams R, Colligan L. Back to basics: checklists in aviation and healthcare. BMJ quality & safety 2015;24:428-431

    Article  Google Scholar 

  5. Mongan J, Kohli M. Artificial intelligence and human life: five lessons for radiology from the 737 MAX disasters. Radiology Artificial intelligence 2020;2

  6. Daye D, Wiggins WF, Lungren MP, et al. Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology 2022:212151

  7. Harolds JA, Parikh JR, Bluth EI, et al. Burnout of radiologists: frequency, risk factors, and remedies: a report of the ACR Commission on Human Resources. Journal of the American College of Radiology 2016;13:411-416

    Article  PubMed  Google Scholar 

  8. Waldman JD, Kelly F, Arora S, et al. The shocking cost of turnover in health care. Health care management review 2010;35:206-211

    Article  PubMed  Google Scholar 

  9. Food, Administration D. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD). 2019

  10. Li D, Morkos J, Gage D, et al. Artificial Intelligence Educational & Research Initiatives and Leadership Positions in Academic Radiology Departments. Current Problems in Diagnostic Radiology 2022

  11. Perchik J, Smith A, Elkassem A, et al. Artificial Intelligence Literacy: Develo** a Multi-institutional Infrastructure for AI Education. Academic Radiology 2022

  12. Congress U. Airline Safety and Federal Aviation Administration Extension Act of 2010. Public Law 2010:111-215

    Google Scholar 

  13. Longridge T, Burki-Cohen JS, Go TH, et al. Simulator fidelity considerations for training and evaluation of today's airline pilots. 2001

  14. Socha V, Socha L, Szabo S, et al. Training of pilots using flight simulator and its impact on piloting precision. Transport Means Juodkrante: Kansas University of Technology 2016:374-379

    Google Scholar 

  15. Aragon CR, Hearst MA. Improving aviation safety with information visualization: a flight simulation study. Proceedings of the SIGCHI conference on Human factors in computing systems; 2005:441–450

  16. Devices T. European Aviation Safety Agency. 2012

  17. Sullivan NJ, Duval-Arnould J, Twilley M, et al. Simulation exercise to improve retention of cardiopulmonary resuscitation priorities for in-hospital cardiac arrests: a randomized controlled trial. Resuscitation 2015;86:6-13

    Article  PubMed  Google Scholar 

  18. Towbin AJ, Paterson BE, Chang PJ. Computer-based simulator for radiology: an educational tool. Radiographics 2008;28:309-316

    Article  PubMed  Google Scholar 

  19. Towbin AJ, Paterson B, Chang PJ. A computer-based radiology simulator as a learning tool to help prepare first-year residents for being on call. Academic radiology 2007;14:1271-1283

    Article  PubMed  Google Scholar 

  20. Shah C, Davtyan K, Nasrallah I, et al. Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for Radiology Trainee Education. Journal of Digital Imaging 2022:1–6

  21. Baltzer PA. Automation Bias in Breast AI. Radiological Society of North America; 2023:e230770

  22. Goddard K, Roudsari A, Wyatt JC. Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association 2012;19:121-127

    Article  PubMed  Google Scholar 

  23. Do HM, Spear LG, Nikpanah M, et al. Augmented radiologist workflow improves report value and saves time: a potential model for implementation of artificial intelligence. Academic radiology 2020;27:96-105

    Article  PubMed  PubMed Central  Google Scholar 

  24. Fischetti C, Bhatter P, Frisch E, et al. The evolving importance of artificial intelligence and radiology in medical trainee education. Academic Radiology 2022;29:S70-S75

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indiana University School of Medicine, Indianapolis, IN, USA

    Umber Shafique

  2. University of Texas Medical Branch, Galveston, TX, USA

    Umar Shafique Chaudhry

  3. Department of Radiology, Cincinnati Children’s Hospital, Cincinnati, OH, USA

    Alexander J. Towbin

  4. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Alexander J. Towbin

Authors
  1. Umber Shafique
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Umar Shafique Chaudhry
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander J. Towbin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study’s conception and design. The first draft of the manuscript was written by Dr. Umber Shafique, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”

Corresponding author

Correspondence to Umber Shafique.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Additional information

Take-Home Points

1. Radiologists should be prepared for the implementation of AI into clinical workflow.

2. Training resources should be developed for radiologists at every level of experience.

3. AI software developers, governing bodies, and radiological societies should collaborate to develop solutions to standardize training and minimize the potential of automation bias and skill erosion.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafique, U., Chaudhry, U.S. & Towbin, A.J. Are the Pilots Onboard? Equip** Radiologists for Clinical Implementation of AI. J Digit Imaging 36, 2329–2334 (2023). https://doi.org/10.1007/s10278-023-00892-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-023-00892-z

Keywords

Search

Navigation