Artificial intelligence could soon help doctors identify children at risk of attention-deficit/hyperactivity disorder (ADHD) years before they receive a formal diagnosis, according to new research that could transform early intervention for one of childhood’s most common neurodevelopmental disorders.
The study, conducted by researchers at Duke Health and published in Nature Mental Health, found that AI can analyse routine electronic health records to detect early warning signs of ADHD long before symptoms typically lead to a diagnosis.
ADHD affects an estimated 8% of children and adolescents. It is characterised by symptoms such as difficulty concentrating, impulsivity, restlessness and problems with organisation. Despite its prevalence, many children are not diagnosed until years after symptoms first appear, delaying access to treatment and support.
Researchers examined health records from more than 140,000 children, including both those diagnosed with ADHD and those without the condition. Using data collected from birth through early childhood, the AI model was trained to identify patterns linked to later ADHD diagnoses.
The system successfully recognised combinations of developmental, behavioural and clinical indicators that often emerged years before a formal diagnosis. It was particularly accurate in assessing risk among children aged five and older, and its performance remained consistent across sex, race, ethnicity and insurance status.
Elliot Hill, the study’s lead author and a data scientist at Duke University School of Medicine, said electronic health records contain a wealth of information that can reveal important patterns.
The researchers believe the technology could help clinicians identify children who may benefit from earlier assessment, allowing families to access support sooner. Early intervention has been shown to improve academic performance, social development and long-term health outcomes.
Naomi Davis, an associate professor in Duke’s Department of Psychiatry and Behavioral Sciences and a co-author of the study, said timely support is critical for children with ADHD.
“Children with ADHD can really struggle when their needs aren’t understood and adequate supports are not in place,” she said.
The research team stressed that the tool is not intended to replace doctors or provide a definitive diagnosis. Instead, it is designed to assist clinicians by highlighting children who may require further evaluation.
Matthew Engelhard, senior author of the study, described it as a way to ensure that children who need help are identified earlier and do not face unnecessary delays.
Experts say the approach could eventually be expanded to other areas of mental health, with similar AI models already being explored to better understand psychiatric risks in adolescents.
The findings also highlight the potential to improve recognition of ADHD in girls, who are often underdiagnosed because their symptoms tend to be less overt than those seen in boys.