Skip to main content
Log in

Leveraging artificial intelligence in radiology education: challenges and opportunities

  • Commentary
  • Published:
European Radiology Aims and scope Submit manuscript

The Original Article was published on 12 August 2023

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

Access this article

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

Instant access to the full article PDF.

References

  1. Kroft LJM, van der Velden L, Giron IH, Roelofs JJH, de Roos A, Geleijns J (2019) Added value of ultra-low-dose computed tomography, dose equivalent to chest X-Ray radiography, for diagnosing chest pathology. J Thorac Imaging 34(3):179–186. https://doi.org/10.1097/rti.0000000000000404

    Article  PubMed  PubMed Central  Google Scholar 

  2. van den Berk IAH, Lejeune EH, Kanglie M et al (2023) The yield of chest X-ray or ultra-low-dose chest-CT in emergency department patients suspected of pulmonary infection without respiratory symptoms or signs. Eur Radiol. https://doi.org/10.1007/s00330-023-09664-3

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chassagnon G, Billet N, Rutten C et al (2023) Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation. Eur Radiol. https://doi.org/10.1007/s00330-023-10043-1

    Article  PubMed  PubMed Central  Google Scholar 

  4. Maleck M, Fischer MR, Kammer B et al (2001) Do computers teach better? A media comparison study for case-based teaching in radiology. Radiographics 21(4):1025–32. https://doi.org/10.1148/radiographics.21.4.g01jl091025

    Article  CAS  PubMed  Google Scholar 

  5. Sarkany D, Deitte L (2017) Providing feedback: practical skills and strategies. Acad Radiol. 24(6):740–6. https://doi.org/10.1016/j.acra.2016.11.023

    Article  PubMed  Google Scholar 

  6. Gefter WB, Hatabu H (2023) Reducing errors resulting from commonly missed chest radiography findings. Chest 163(3):634–49. https://doi.org/10.1016/j.chest.2022.12.003

    Article  PubMed  Google Scholar 

  7. Garin SP, Zhang V, Jeudy J, Parekh VS, Yi PH (2023) Systematic review of radiology residency artificial intelligence curricula: preparing future radiologists for the artificial intelligence era. J Am Coll Radiol. 20(6):561–9. https://doi.org/10.1016/j.jacr.2023.02.031

    Article  PubMed  Google Scholar 

  8. Essa SG, Celik T, Human-Hendrick NE (2023) Personalized adaptive learning technologies based on machine learning techniques to identify learning styles: a systematic literature review. IEEE Access 11:48392–48409. https://doi.org/10.1109/ACCESS.2023.3276439

    Article  Google Scholar 

  9. Duong MT, Rauschecker AM, Rudie JD et al (2019) Artificial intelligence for precision education in radiology. Br J Radiol 92(1103):20190389. https://doi.org/10.1259/bjr.20190389

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The author states that this work has not received any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Constance de Margerie-Mellon.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Constance de Margerie-Mellon

Conflict of interest

The author of this manuscript declares no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Not applicable

Informed consent

Not applicable

Ethical approval

Not applicable

Study subjects or cohorts overlap

Not applicable

Methodology

• Commentary

Additional information

Publisher's note

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

This comment refers to the article available at https://doi.org/10.1007/s00330-023-10043-1.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Margerie-Mellon, C. Leveraging artificial intelligence in radiology education: challenges and opportunities. Eur Radiol 33, 8239–8240 (2023). https://doi.org/10.1007/s00330-023-10112-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10112-5

Navigation