| |  Online-Ressource |
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| Verfasst von: | Busch, Felix [VerfasserIn]  |
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| | Hoffmann, Lena [VerfasserIn] |
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| | dos Santos, Daniel Pinto [VerfasserIn] |
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| | Makowski, Marcus R. [VerfasserIn] |
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| | Saba, Luca [VerfasserIn] |
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| | Prucker, Philipp [VerfasserIn] |
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| | Hadamitzky, Martin [VerfasserIn] |
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| | Navab, Nassir [VerfasserIn] |
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| | Kather, Jakob Nikolas [VerfasserIn] |
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| | Truhn, Daniel [VerfasserIn] |
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| | Cuocolo, Renato [VerfasserIn] |
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| | Adams, Lisa C. [VerfasserIn] |
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| | Bressem, Keno K. [VerfasserIn] |
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| Titel: | Large language models for structured reporting in radiology |
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| Titelzusatz: | past, present, and future |
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| Verf.angabe: | Felix Busch, Lena Hoffmann, Daniel Pinto dos Santos, Marcus R. Makowski, Luca Saba, Philipp Prucker, Martin Hadamitzky, Nassir Navab, Jakob Nikolas Kather, Daniel Truhn, Renato Cuocolo, Lisa C. Adams and Keno K. Bressem |
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| E-Jahr: | 2025 |
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| Jahr: | 23 October 2024 |
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| Umfang: | 14 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 03.06.2025 |
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| Titel Quelle: | Enthalten in: European radiology |
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| Ort Quelle: | Berlin : Springer, 1991 |
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| Jahr Quelle: | 2025 |
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| Band/Heft Quelle: | 35(2025), 5, Seite 2589-2602 |
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| ISSN Quelle: | 1432-1084 |
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| | 1613-3757 |
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| Abstract: | Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. |
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| DOI: | doi:10.1007/s00330-024-11107-6 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1007/s00330-024-11107-6 |
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| | kostenfrei: Volltext: https://link.springer.com/article/10.1007/s00330-024-11107-6 |
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| | DOI: https://doi.org/10.1007/s00330-024-11107-6 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Computational Linguistics |
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| | Dental Radiology |
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| | Electronic data processing |
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| | Information Model |
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| | Medical informatics |
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| | Natural language processing |
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| | Natural Language Processing (NLP) |
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| | Radiography |
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| | Radiology |
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| K10plus-PPN: | 1927375827 |
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| Verknüpfungen: | → Zeitschrift |
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Large language models for structured reporting in radiology / Busch, Felix [VerfasserIn]; 23 October 2024 (Online-Ressource)
