ReviewMonatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde2021
[Artificial intelligence in image evaluation and diagnosis].
Review in Monatsschrift Kinderheilkunde : Organ der Deutschen Gesellschaft fur Kinderheilkunde, 2021. 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.
- Application of deep neural networks in automatized ventriculometry and segmentation of the aqueduct in pediatric hydrocephalus patients.Neuroradiology · 2026Article
- Article
- Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study.JMIR formative research · 2024Article
- Does the Information Quality of ChatGPT Meet the Requirements of Orthopedics and Trauma Surgery?Cureus · 2024Article
- Transparent human - (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiology is characterized by constant change and is defined by technological progress. Artificial intelligence (AI) will change all aspects of practical work in pediatric radiology in the future. Image acquisition, diagnosis recognition and segmentation as well as the recognition of tissue properties and their combination with big data will be the main areas of application in radiology. Higher effectiveness, acceleration of investigations and making the diagnosis as well as cost savings are expectations associated with the use of AI. Improved patient management, easier work for radiology assistants and pediatric radiologists as well as faster examination and diagnosis times mark the milestones of AI development in radiology. From appointment communication and device control to treatment recommendations and monitoring, the daily life will be changed by elements of AI. Pediatric radiologists must therefore be informed about the basics of AI and work together with data scientists in establishing and using AI elements.
Indexed as
Identifiers
What 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.