Large language model-based information extraction from free-text radiology reports: a scoping review protocol.

ABSTRACT:

Introduction

Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP). Recently, a new approach to NLP, large language models (LLMs), has gained momentum and continues to improve performance of IE-related tasks. The objective of this scoping review is to show the state of research regarding IE from free-text radiology reports based on LLMs, to investigate applied methods and to guide future research by showing open challenges and limitations of current approaches. To our knowledge,

SUBMITTER: Reichenpfader D 

PROVIDER: S-EPMC10729196 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature