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Updated: Jan 23, 2026
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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
Published on: October 13, 2018
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[Natural language processing in radiology : Neither trivial nor impossible]
F Jungmann1, S Kuhn2, I Tsaur3
1Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland. florian.jungmann@unimedizin-mainz.de.
Der Radiologe
|June 7, 2019
Summary
Natural Language Processing (NLP) in radiology offers a way to generate databases for quality assurance and process optimization. Effective NLP requires specific tasks, intuitive use, and transparent quality metrics for extracting valuable information from free text.
Area of Science:
- Radiology
- Medical Informatics
- Natural Language Processing
Background:
- The demand for Natural Language Processing (NLP) expertise is growing in radiology.
- NLP complements structured reporting, enabling database generation for quality assurance and process optimization.
Purpose of the Study:
- To outline the possibilities and challenges of applying NLP in radiology.
- To identify and demonstrate the requirements and expectations for NLP systems in this field.
Main Methods:
- Requirements and expectations for NLP systems were identified.
- A case study was used to demonstrate these requirements.
Main Results:
- Effective NLP use in radiology necessitates specific interpretation tasks (e.g., using RadLex).
- Intuitive usage, feedback mechanisms, and transparent quality assessment are crucial for NLP results.
Conclusions:
- Suitable NLP systems can extract targeted information from extensive free-text data.
- This extraction can be achieved with manageable manual effort and high accuracy.
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