ReviewRadiologie (Heidelberg, Germany)2023
[Validation and implementation of artificial intelligence in radiology : Quo vadis in 2022?]
Review in Radiologie (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).Biomedical reports · 2025Review
- [Influence of artificial intelligence on decisions in radiology using the example of lung cancer screening].Radiologie (Heidelberg, Germany) · 2025Article
- [Artificial intelligence in radiology : Literature overview and reading recommendations].Radiologie (Heidelberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hype around artificial intelligence (AI) in radiology continues and the number of approved AI tools is growing steadily. Despite the great potential, integration into clinical routine in radiology remains limited. In addition, the large number of individual applications poses a challenge for clinical routine, as individual applications have to be selected for different questions and organ systems, which increases the complexity and time required.
objectivesThis review will discuss the current status of validation and implementation of AI tools in clinical routine, and identify possible approaches for an improved assessment of the generalizability of results of AI tools. MATERIALS AND
methodsA literature search in various literature and product databases as well as publications, position papers, and reports from various stakeholders was conducted for this review.
resultsScientific evidence and independent validation studies are available for only a few commercial AI tools and the generalizability of the results often remains questionable.
conclusionsOne challenge is the multitude of offerings for individual, specific application areas by a large number of manufacturers, making integration into the existing site-specific IT infrastructure more difficult. Furthermore, remuneration for the use of AI tools in clinical routine by health insurance companies in Germany is lacking. But in order for reimbursement to be granted, the clinical utility of new applications must first be proven. Such proof, however, is lacking for most applications.
Indexed as
Identifiers
36510007What 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.