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…
AI research is moving ADHD diagnosis away from subjective interviews toward objective, data-driven tools, with EEG emerging as preferred modality for recent models.
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, v1: “From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024).” Truvace, 2026-08-03. /record/TRV-2026-0630 (accessed at citation time). sha256 81a27964afa69e26…
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