AI Predicts Childhood ADHD Risk Years Before Diagnosis Using Routine Health Records

Artificial intelligence tools can analyze routine electronic health records to accurately estimate a child’s risk of developing attention-deficit/hyperactivity disorder (ADHD) years before a typical diagnosis, researchers report.

ADHD affects millions of children, yet many go years without a diagnosis, missing the chance for early support that can change long-term outcomes even when early signs are present. According to the new study, by reviewing patterns in everyday medical data, the approach could help flag children who may benefit from earlier evaluation and follow-up.

The research, published in Nature Mental Health, highlights how powerful insights can come from information already collected during regular health care visits to help support early decision-making by primary care providers.

“We have this incredibly rich source of information sitting in electronic health records. The idea was to see whether patterns hidden in that data could help us predict which children might later be diagnosed with ADHD, well before that diagnosis usually happens.”

The study, led by Elliot Hill, lead author and data scientist in the biostatistics and bioinformatics department at Duke University School of Medicine, analyzed electronic health records from more than 140,000 children, with and without ADHD. Researchers trained a specialized AI model to examine medical history from birth through early childhood, identifying combinations of developmental, behavioral, and clinical events that often appeared years before an ADHD diagnosis.

The model demonstrated high accuracy in estimating future ADHD risk in children aged 5 and older, with consistent performance across patient characteristics such as sex, race, ethnicity, and insurance status. Importantly, the tool does not diagnose ADHD but identifies children who may benefit from closer attention by their pediatric primary care provider or an earlier referral for ADHD assessment by a specialist.

“This is not an AI doctor. It’s a tool to help clinicians focus their time and resources, so kids who need help don’t fall through the cracks or wait years for answers.”

The researchers emphasize that earlier identification for screening could lead to earlier diagnosis and, consequently, earlier support—linked to better academic, social, and health outcomes for children with ADHD. They stress the need for further studies before such tools are implemented in clinical settings.

“Children with ADHD can really struggle when their needs aren’t understood and adequate supports are not in place,” says Naomi Davis, study author and associate professor in the psychiatry and behavioral sciences department at Duke. “Connecting families with timely, evidence-based interventions is essential for helping them achieve their goals and laying a foundation for future success.”

Hill and Engelhard have also explored AI models for predicting risks and causes of mental illness in adolescents. The study was supported by grants from the National Institute of Mental Health and the National Center for Advancing Translational Sciences.