aceFrom machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024)
Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI). Objective This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions. Methods A structured search of the Scopus database ret…
A bibliometric review of 722 papers found AI research on ADHD prediction grew exponentially, peaking in 2023, with a shift from SVMs to deep learning and EEG.
High algorithmic accuracy in AI models for ADHD has not yet translated into routine clinical utility without advances in explainability and multimodal fusion.
Evidence
- Peer-reviewedApplied Neuropsychology: Adult2026-08-01
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Truvace Impact Record TRV-2026-0630, v2: “From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).” Truvace, 2026-10-26. /record/TRV-2026-0630 (accessed at citation time). sha256 1f9a60e2c53feee5…
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