Skip to main content
SpringerLink
Log in

Transformers, codes and labels: large language modelling for natural language processing in clinical radiology

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

The Original Article was published on 11 March 2023

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Bain E (2023) How to use ai essay writer ChatGPT to write your papers in seconds. HITC. https://www.hitc.com/en-gb/2022/12/08/how-to-use-ai-essay-writer-chatgpt-to-write-your-papers-in-seconds/. Accessed on 8.2.23

  2. Mazzeschi M (2021) Encoding data with transformers. Towards Data Science. https://towardsdatascience.com/encoding-data-with-transformers-d14445e96ead Accessed on 8.2.23

  3. Vaswani A, Shazeer N, Parmar N et al (2017) Attention Is All You Need. arXiv:1706.03762 [cs.CL]. Accessed on 8.2.23

  4. Briggs J (2021) Masked-language modeling with BERT. Towards Data Science. https://towardsdatascience.com/masked-language-modelling-with-bert-7d49793e5d2c. Accessed on 8.2.23

  5. Faes L, Wagner SK, Fu DJ et al (2019) Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health 1(5):e232-e242. https://doi.org/10.1016/S2589-7500(19)30108-6. Accessed on 8.2.23

  6. Richens JG, Lee CM, Johri S (2020) Improving the accuracy of medical diagnosis with causal machine learning. Nat Commun 11:3923. https://doi.org/10.1038/s41467-020-17419-7. (Accessed on 8.2.23)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wagholikar KB, Estiri H, Murphy M, Murphy SN (2020) Polar labeling: silver standard algorithm for training disease classifiers. Bioinformatics 36(10):3200–3206. https://doi.org/10.1093/bioinformatics/btaa088.. Accessed on 8.2.23

  8. Nowak S, Biesner D, Layer YC et al (2023) Transformer-based structuring of free-text radiology report databases. Eur Radiol. https://doi.org/10.1007/s00330-023-09526-y

  9. Shen Y, Heacock L, Elias J, Hentel KD, Reig B, Shih G, Moy L (2023) ChatGPT and other large language models are double-edged swords. Radiology. https://doi.org/10.1148/radiol.230163. Accessed on 8.2.23

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Northwick Park Hospital, Harrow, HA1 3UJ, UK

    Denis Remedios

  2. Treebeardtech, London, UK

    Alex Remedios

Authors
  1. Denis Remedios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alex Remedios
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Denis Remedios.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Denis Remedios.

Conflict of interest

D. Remedios is a member of the European Radiology Scientific Editorial Board. He has not taken part in the review or selection process of this article.

The remaining author of this manuscript declare no conflict of interest.

Statistics and biometry

Not applicable.

Informed consent

Not applicable.

Ethical approval

Not applicable.

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/s00330-023-09526-y

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Remedios, D., Remedios, A. Transformers, codes and labels: large language modelling for natural language processing in clinical radiology. Eur Radiol 33, 4226–4227 (2023). https://doi.org/10.1007/s00330-023-09566-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09566-4

Search

Navigation