ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2025
[Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].
Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of artificial intelligence (AI) in pediatric and adolescent medicine offers numerous possibilities, particularly in the prevention of chronic diseases. AI-powered applications such as machine learning for the analysis of speech or movement patterns can, for example, help in the early diagnosis of autism spectrum disorders or motor development delays. In addition, AI-based systems support the treatment of children with type 1 diabetes through automated insulin dosing (AID) systems.AI enables more accurate diagnoses and personalized therapeutic approaches and helps relieve the burden on medical personnel. At the same time, there are challenges associated with the use of AI, which is why only a few applications have so far become part of routine clinical practice. These challenges include the protection of sensitive data and the respect for informational self-determination, ensuring freedom from discrimination, algorithmic transparency, and the acceptance of AI by all involved groups such as children, adolescents, parents, and medical professionals. All stakeholders express concerns about potential misjudgments, the loss of personal interactions, and the possible commercial use of data. Parents and professionals emphasize the importance of clear communication, shared decision-making, and training to promote better understanding. Moreover, there is often a lack of structured, high-quality, large datasets in compatible formats to effectively train AI systems.A sustainable integration of AI in pediatric and adolescent medicine requires large-scale clinical studies, access to high-quality datasets, and a nuanced analysis of the ethical and social implications.
Indexed as
Identifiers
What Socratic 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 Socratic graph.