ArticleHealth science reports2025
Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.
Article in Health science reports, 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.
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
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
Background and Aims: Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in childhood and adolescence, and its early diagnosis is essential for preventing long-term cognitive and behavioural issues. Artificial intelligence (AI), as an emerging tool, holds significant potential for early prediction and detection of this disorder through analyzing clinical and behavioral data. This study reviews recent research on how AI is used to predict ADHD in children and adolescents. Methods: The PubMed, Scopus, Web of Science, and Embase databases were searched for articles on the use of artificial intelligence to predict ADHD through October 14, 2025. Data were collected using an extraction form based on the PRISMA-ScR guidelines, and the findings were presented in figures and tables. Results: A total of 3981 records were identified through database searches, reduced to 1935 after duplicates were removed. Ultimately, 42 studies were included. The most frequently used AI methods were Random Forest (RF) ( Conclusion: AI, especially machine learning models like RF and LR, shows great potential for predicting and managing ADHD in children and teens. These methods could act as helpful tools for early diagnosis, leading to better cognitive, behavioral, and educational outcomes related to the disorder. However, the clinical translation of these AI-based approaches requires attention to interpretability, workflow integration, and ethical considerations to ensure their safe and practical use in real-world settings.
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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.