DURHAM, N.C. — Attention-deficit/hyperactivity disorder (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.

In a new study, Duke Health researchers found that artificial intelligence tools can analyze routine electronic health records to accurately estimate a child’s risk of developing ADHD years before a typical diagnosis. 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 Health¹ on April 27, 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.

“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,” said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine. 

To arrive at the findings, researchers analyzed electronic health records from more than 140,000 children, with and without ADHD. They trained a specialized AI model to look at medical history from birth through early childhood. The model learned to recognize combinations of developmental, behavioral, and clinical events that often appeared years before an ADHD diagnosis was made.

The model was highly accurate at estimating future ADHD risk in children age 5 and older, with consistent performance across patient characteristics like sex, race, ethnicity, and insurance status. 

Importantly, the tool does not make a diagnosis. It 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,” said Matthew Engelhard, M.D., Ph.D., in Duke’s Department of Biostatistics & Bioinformatics, and senior author of the study. “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 note that earlier identification for screening could lead to earlier diagnosis and therefore earlier support, which is linked to better academic, social, and health outcomes for children with ADHD. They also emphasize the need for further studies before such tools are used in clinical settings.

“Children with ADHD can really struggle when their needs aren’t understood and adequate supports are not in place,” said study author, Naomi Davis, Ph.D., associate professor in the Department of Psychiatry and Behavioral Sciences. “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 researched the use of AI models in predicting potential risks and causes for mental illness in adolescents. In addition to Hill Engelhard, and Davis, the authors for this study include De Rong Loh, Benjamin A. Goldstein, and Geraldine Dawson.

Reference:

1) https://www.nature.com/articles/s44220-026-00628-2

(Newswise/HG)