ReviewDer Radiologe2021
[Structured reporting and artificial intelligence].
Review in Der Radiologe, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Leveraging AI-based digital systems in psychological interventions: a research opinion.Frontiers in digital health · 2025Article
- A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.Insights into imaging · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfolio of available AI algorithms for, e.g. automatic detection and preselection of pathologic patterns in images or for facilitating the reporting workflows. Even machines already use AI algorithms for improvement of operating comfort.
methodThe use of SR is essential especially for the extraction of automatically evaluable semantic data from radiology results reports. Regarding eligibility in certification processes, the use of SR is mandatory for the accreditation of the German Cancer Society as an oncological center or outside Germany, such as the European Cancer Center.
resultsThe data from SR can be automatically evaluated for the purpose of patient care, research and educational purposes and quality assurance. Lack of information and a high degree of variability often hamper the extraction of valid information from free-text reports using neurolinguistic programming (NLP). Against the background of supervised training, AI algorithms or k‑nearest neighbors (KNN) require a considerable amount of validated data. The semantic data from SR can also be processed by AI and used for training.
conclusionThe AI and SR are separate entities within the field of radiology with mutual dependencies and significant added value. Both have a high potential for profound upcoming changes and further developments in radiology.
Indexed as
Identifiers
34605945What 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.