Editorial & Opinion
Integrating large language models into radiological practice.
| Τίτλος: | Integrating large language models into radiological practice. |
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| Συγγραφείς: | van Assen M; Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA. Electronic address: marly.v.assen@gmail.com., Muscogiuri E; Department of Radiology, UZ Leuven, Belgium., De Cecco CN; Department of Radiology, UZ Leuven, Belgium. |
| Πηγή: | European journal of radiology [Eur J Radiol] 2026 Oct; Vol. 203, pp. 112983. Date of Electronic Publication: 2026 Jun 02. |
| Τύπος έκδοσης: | Editorial |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Ireland Ltd Country of Publication: Ireland NLM ID: 8106411 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7727 (Electronic) Linking ISSN: 0720048X NLM ISO Abbreviation: Eur J Radiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Science Ireland Ltd Original Publication: Stuttgart ; New York : Thieme, [c1981- |
| Ιατρικοί όροι (MeSH): | Radiology*/methods , Radiology*/organization & administration , Decision Support Systems, Clinical*/organization & administration , Large Language Models* , Artificial Intelligence*, Generative Artificial Intelligence ; Humans ; Systems Integration |
| Περίληψη: | Purpose: To highlight the potential of large language models (LLMs) in radiology and to stimulate discussion on their integration into clinical practice, including associated benefits and challenges. Methods: This editorial was authored with the assistance of an advanced artificial intelligence (AI) language model (ChatGPT-4, OpenAI), under direct human oversight. The content was critically reviewed, edited, and refined by the author, and all references were verified by the editors. Results: LLMs demonstrate substantial potential to enhance radiological practice, including improvements in workflow efficiency, reporting, education, and clinical decision support. However, their implementation raises important considerations related to accuracy, reliability, ethical use, and appropriate clinical oversight. Conclusion: The integration of LLMs into radiology requires a balanced approach that embraces their innovative capabilities while ensuring ethical, safe, and practical implementation. With appropriate safeguards and continued evaluation, LLMs may contribute to improved patient care and outcomes. (Copyright © 2026 Elsevier B.V. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Marly Van Assen, PhD reports financial support was provided by Cleerly Inc. Marly Van Assen, PhD reports financial support was provided by Siemens Healthineers. Marly Van Assen, PhD reports financial support was provided by Lucentia Inc. Carlo N De Cecco, MD, PhD reports financial support was provided by Bayer Corporation. Carlo N De Cecco, MD, PhD reports financial support was provided by Siemens Healthineers. Carlo N De Cecco, MD, PhD reports financial support was provided by Elucid Bioimaging Inc. Carlo N De Cecco, MD, PhD reports financial support was provided by Cleerly Inc. Both, Carlo N De Cecco, MD, PhD and Marly Van Assen, PhD are on the editorial board of the European Journal of Radiology. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper]. |
| Contributed Indexing: | Keywords: Artificial Intelligence; Automated Reporting; Clinical decision support; Large language models; Radiology |
| Entry Date(s): | Date Created: 20260616 Date Completed: 20260809 Latest Revision: 20260811 |
| Update Code: | 20260811 |
| DOI: | 10.1016/j.ejrad.2026.112983 |
| PMID: | 42302435 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1872-7727 |
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| DOI: | 10.1016/j.ejrad.2026.112983 |