ReviewDer Radiologe2018
[Basics and applications of Natural Language Processing (NLP) in radiology].
Review in Der Radiologe, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Artificial intelligence based on forensic medical databases: construction, application, and prospects.Forensic science, medicine, and pathology · 2026Review
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.BMC medical informatics and decision making · 2021Article
- [Cross-enterprise interoperability : Challenges and principles for technical implementation].Der Radiologe · 2020Review
- [Natural language processing in radiology : Neither trivial nor impossible].Der Radiologe · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to the increasing demands in radiology, applications that enable quality assurance and continuous process optimization are required.
objectiveThe principles of Natural Language Processing (NLP) as a computer-based method for structuring of free text reports are explained and application scenarios are sketched. MATERIALS UND
methodsThe structuring of free texts succeeds by several theories, linguistic techniques (word meanings, word context, negations), statistical methods with rules and currently with deep learning approaches. Medical encyclopedias, such as RadLex®, are suitable for coding findings. NLP was used in our own radiology clinic to check the quality of 3756 CT reports.
resultsIn our case study, NLP proved to be a helpful, automated tool for internal quality testing. DISCUSSION: NLP offers numerous application scenarios for decision support and for quality management in radiology.
Indexed as
Identifiers
30014451What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.