ArticleFrontiers in psychiatry2023
Diagnosing attention-deficit hyperactivity disorder (ADHD) using artificial intelligence: a clinical study in the UK.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled 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.
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
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications.Frontiers in human neuroscience · 2025Pooled it
- Individualized prediction models in ADHD: a systematic review and meta-regression.Molecular psychiatry · 2024Pooled it
- Exploring the Association Between Social Determinants of Health and Telehealth Utilization for Attention-Deficit/Hyperactivity Disorder Among Adults Using Machine Learning: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.Scientific reports · 2025Article
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
- Point of Care Testing, Rapid Next Generation Sequencing and Artificial Intelligence in Pediatric and Neonatal Healthcare: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Review
- What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Integrating explainable AI with clinical features to enhance ADHD diagnostic understanding.Frontiers in psychiatry · 2025Article
- Revolutionizing attention deficit hyperactivity disorder with artificial intelligence.PLOS mental health · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learning model and a knowledge-based model) for assisting adult ADHD diagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future.
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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.