Artificial Intelligence, Machine Learning, and Deep Learning Approaches for Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for a Scoping Review

Background

Attention-deficit/hyperactivity disorder (ADHD) affects over 366 million adults and 139 million children worldwide, yet diagnosis remains fundamentally subjective, relying on clinical interviews, behavioral observations, and rating scales that yield inconsistent results across practitioners and settings. Artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer a paradigm shift toward objective, data-driven diagnosis by detecting complex patterns across neuroimaging, electrophysiology, and digital biomarkers that elude conventional assessment. Although AI-based ADHD research has grown exponentially, no comprehensive synthesis examines the full spectrum of data modalities, validation practices, and clinical translation readiness. This gap limits our understanding of which approaches are most promising for real-world implementation.