Reichenpfader, Daniel; Müller, Henning; Denecke, Kerstin (2024). A scoping review of large language model based approaches for information extraction from radiology reports npj Digital Medicine, 7(1) Springer Nature 10.1038/s41746-024-01219-0
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Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science > Institute for Patient-centered Digital Health > AI for Health School of Engineering and Computer Science |
Name: |
Reichenpfader, Daniel 0000-0002-8052-3359; Müller, Henning and Denecke, Kerstin 0000-0001-6691-396X |
Subjects: |
Q Science > Q Science (General) R Medicine > R Medicine (General) T Technology > T Technology (General) |
ISSN: |
2398-6352 |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Kerstin Denecke |
Date Deposited: |
11 Sep 2024 09:52 |
Last Modified: |
11 Sep 2024 09:52 |
Publisher DOI: |
10.1038/s41746-024-01219-0 |
ARBOR DOI: |
10.24451/arbor.22416 |
URI: |
https://arbor.bfh.ch/id/eprint/22416 |
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