Evidence mapPaperPMID 40586805Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2025

[Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].

Janna-Lina Kerth, Anne Christine Bischops, Maurus Hagemeister, Lisa Reinhart, Kerstin Konrad, Bert Heinrichs, Thomas Meissner

Abstract readEnglish AbstractReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Janna-Lina KerthKlinik für Allgemeine Pädiatrie, Neonatologie und Kinderkardiologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland. janna-lina.kerth@med.uni-duesseldorf.de.
Anne Christine BischopsKlinik für Allgemeine Pädiatrie, Neonatologie und Kinderkardiologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.
Maurus HagemeisterKlinik für Allgemeine Pädiatrie, Neonatologie und Kinderkardiologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.
Lisa ReinhartKlinik für Allgemeine Pädiatrie, Neonatologie und Kinderkardiologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.
Kerstin KonradKlinik für Psychiatrie, Psychosomatik und Psychotherapie des Kindes- und Jugendalters, Uniklinik Aachen, Aachen, Deutschland.
Bert HeinrichsInstitut für Neurowissenschaften und Medizin: Gehirn und Verhalten (INM-7), Forschungszentrum Jülich, Jülich, Deutschland.
Thomas MeissnerKlinik für Allgemeine Pädiatrie, Neonatologie und Kinderkardiologie, Medizinische Fakultät und Universitätsklinikum Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Adolescent MedicineArtificial IntelligencePediatricsPreventive MedicineAdolescentChildFemaleGermanyHumansMaleArtificial intelligenceDevelopmental disordersDisease preventionPediatric and adolescent medicineTechnology acceptance

Identifiers

PMID40586805
PMCPMC12287224

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.