Large-Scale Assessment of Language, Speech, and Movement in Autism and ADHD with AI

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) frequently co-occur with overlapping traits. We investigated whether automated behavioral analysis during a clinician-child interview can identify distinct, objective features of the two conditions. Analyzing audio-video recordings of 2,529 youths (ages 5-22) in a broad community sample, multivariate models revealed that language difficulties often attributed to ADHD are primarily explained by age, cognitive ability, or co-occurring ASD. Increased motor activity specifically marked hyperactive-impulsive ADHD, but not ASD or inattentive ADHD. ASD was uniquely characterized by divergent narrative production and idiosyncratic responses, alongside a distinct vocal profile of higher pitch and dysphonia, despite structurally intact language. While these digital behavioral measures correlate with most diagnostic categories and age, the joint analysis effectively separates the effects of ASD from ADHD. These findings show that scalable digital assessment from recorded clinical interviews can disentangle overlapping ASD and ADHD diagnoses into domain-specific behavioral signatures.