AI predicts ADHD in children by age 5, years before most get diagnosed

AI Turn "Hidden Patterns" Into ADHD Insights - Neuroscience News

Summary: A new study demonstrates that artificial intelligence can accurately estimate a child's risk of developing ADHD years before a clinical diagnosis occurs. By mining "hidden patterns" in routine Electronic Health Records (EHR) from birth through early childhood, the AI identifies combinations of developmental and behavioral markers that human clinicians might overlook during brief visits. This tool is designed to act as a "clinical safety net," ensuring at-risk children receive early evaluations and support during critical developmental windows. 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 Health 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. "We have this incredibly rich source of information sitting in electronic health records," said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine. "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." 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. Funding: The study was supported by grants from the National Institute of Mental Health (K01-MH127309, UL1 TR002553) and National Center for Advancing Translational Sciences. Author: Stephanie Lopez Source: Duke University Contact: Stephanie Lopez - Duke University Image: The image is credited to Neuroscience News Original Research: Closed access. "Fetal and postnatal metal metabolism-related changes in brain function are associated with childhood behavioral deficits" by Elliot D. Hill, De Rong Loh, Naomi O. Davis, Benjamin A. Goldstein, Geraldine Dawson & Matthew Engelhard. Nature Mental Health DOI:10.1038/s44220-026-00628-2 Abstract Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition that can negatively impact long-term outcomes for individuals. Early diagnosis is critical, yet demographic and clinical disparities can delay detection. Using electronic health records (EHRs) from a cohort of over 720,000 patients, we pretrained an EHR foundation model. We then fine tuned it to predict the likelihood of ADHD diagnosis and timing from birth until age 9 years in a pediatric cohort of over 140,000 patients. By age 5 years, the model achieved a time-dependent area under the receiver operating characteristic curve of 0.92 at a 4-year time horizon. Overall, the model maintained its performance across patients with differing demographics, including sex, race, ethnicity and insurance status. Our feature importance analysis found that ADHD was strongly associated with developmental, behavioral and psychiatric conditions. Our results suggest that EHR-based predictive models could help providers reliably identify children with ADHD in a timely manner.

AI tools can predict ADHD risk years before a formal diagnosis

Duke University Medical CenterApr 27 2026 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 Health 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. 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." Elliot Hill, lead author of the study and data scientist, Department of Biostatistics & Bioinformatics, 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. The study was supported by grants from the National Institute of Mental Health (K01-MH127309, UL1 TR002553) and National Center for Advancing Translational Sciences. Duke University Medical Center Journal reference: Hill, E. D., et al. (2026). Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records. Nature Mental Health. DOI: 10.1038/s44220-026-00628-2. https://www.nature.com/articles/s44220-026-00628-2

AI may spot ADHD years before kids get diagnosis

Artificial intelligence tools can analyze routine electronic health records to accurately estimate a child's risk of developing ADHD years before a typical diagnosis, researchers report. 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. 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 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," says Elliot Hill, lead author of the study and data scientist in the biostatistics and bioinformatics department at Duke University School of Medicine. "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." 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," says Matthew Engelhard of Duke's biostatistics and bioinformatics department, 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," says study author, Naomi Davis, associate professor in the psychiatry and behavioral sciences department. "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. The study was supported by grants from the National Institute of Mental Health and National Center for Advancing Translational Sciences.

AI can predict ADHD in children years before diagnosis

A computer tool can predict which children will later receive an ADHD diagnosis years before many families get answers, according to new research. That early warning could move children toward support while school, friendships, and daily confidence are still taking shape. In routine pediatric records from birth through early childhood, everyday visits carried clues that often appeared before ADHD became official. By tracing those clues, Elliot D. Hill at Duke University School of Medicine (Duke Med) connected early patterns to later diagnoses. Those patterns did not depend on one warning sign, because the software recognized recurring combinations across development, behavior, and care. That makes the finding useful as a screening flag, not as proof that a child has the condition. Doctors call the condition ADHD - short for attention-deficit/hyperactivity disorder - when inattention, overactivity, or impulsive behavior disrupt daily life. A national survey found diagnosed ADHD in 10.47% of U.S. children ages 4 to 17 during 2021 to 2022. Many children show struggles earlier, but families may not reach an assessment until school problems pile up. Earlier screening can start parent training, classroom supports, and coping plans while children still build basic habits. Routine care left a long trail in electronic health records - digital files that track care over time. Within those records, repeated mentions of development, behavior, sleep, medicine, and referrals created a longer view of each child's needs. "We have this incredibly rich source of information sitting in electronic health records," said Hill. That richness matters because routine care may capture concerns before anyone labels them as ADHD, and records still need privacy protections. To build the artificial intelligence tool, the researchers first trained it on records from more than 720,000 patients. They then adapted the system for more than 140,000 children whose records tracked care from birth to age nine. That two-step training let the model learn common medical sequences before judging each child's later ADHD risk. Because the data came from past care, the tool needs testing in new settings before anyone uses it for patient care. By age five, the model reached a 0.92 ranking score for predicting diagnoses up to four years later. On a scale where 1.0 separates every later case correctly, 0.92 shows a strong sorting signal. Still, a high score does not say a child has ADHD, because diagnosis requires clinical judgment. For a pediatrician, the useful outcome is a shorter list of children who deserve closer follow-up before problems get worse. Across sex, race, ethnicity, and insurance status, the model kept similar performance in the Duke records. Balanced performance matters because screening tools can do harm when they miss one group more often than another. Even so, one health system cannot prove fairness for every community, insurance network, or clinic workflow. Future testing should show whether the same alerts help children without widening existing gaps, especially where specialists are scarce and appointments take months. When the team checked which record events influenced predictions most, developmental and behavioral concerns stood out. Those entries can reflect speech delays, learning concerns, emotional symptoms, or repeated visits about attention and behavior. The model also found links with psychiatric conditions, meaning mental health patterns sometimes traveled alongside later ADHD diagnoses. Such clues can guide follow-up, but they do not prove that any one event caused the condition on its own. A prediction tool works best when it gives busy clinicians a clear reason to look more closely. "This is not an AI doctor," said Matthew Engelhard, M.D., Ph.D., senior author and biostatistics researcher at Duke University School of Medicine. Clinicians still need family interviews, teacher reports, developmental history, and judgment before making an ADHD diagnosis or referral. Without that human step, a risk score could become a label instead of a prompt for better care. Earlier concern gives families more time to learn strategies before repeated frustration shapes a child's school life. The American Academy of Pediatrics supports family, school, and medical input in its guideline when planning care. Strong supports can change daily routines by making expectations clearer and helping adults respond consistently at home and school. That practical help is the real promise, if alerts lead to care rather than worry or watchful waiting alone. Routine records, careful modeling, and clinical follow-up could turn scattered early concerns into timely attention for children who might otherwise wait. For now, the tool points toward smarter screening, while privacy, fairness, and real-world testing remain essential safeguards. Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates.

Duke study: AI model detects ADHD in kids as young as 5

Why it matters: That's years earlier than most kids are diagnosed, an example of how AI tools that analyze large datasets could reshape health care in the coming decades. Driving the news: A paper published Monday by Nature Mental Health showed the new AI model for detecting attention-deficit/hyperactivity disorder works by age 5. * That is "well before that diagnosis usually happens," lead author Elliot Hill, a data scientist in the Duke University School of Medicine, said in a news release. How it works: Researchers fed the data from 140,000 children (birth to age 9) to an AI model already trained on the electronic health records of more than 720,000 people. * The AI model picked up on hidden patterns in the data and could accurately predict a diagnosis across demographics like insurance status, sex and race, the paper says. What they're saying: "This is not an AI doctor," study co-author Matthew Engelhard, a fellow Duke data scientist, said in the release. "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." * Naomi Davis, a co-author from the field of psychiatry and behavioral sciences, added that "children with ADHD can really struggle when their needs aren't understood and adequate supports are not in place." By the numbers: Around 11% of American children are diagnosed with ADHD, according to 2022 data analyzed by the Centers for Disease Control and Prevention. * That's 15% of boys and 8% of girls. The intrigue: Just over half (54%) of U.S. children diagnosed with ADHD were taking medication, per CDC data. * Meanwhile, demand for ADHD medications has surged in recent years among adults, causing drug shortages at times. What's next: The researchers say the model, while promising, should undergo further study before it can be used in the real world.

Could AI help spot ADHD in children before symptoms fully emerge?

Researchers found that the AI system could accurately flag early warning signs of ADHD, potentially allowing children to receive support much sooner. Artificial intelligence could help identify children at risk of attention-deficit/hyperactivity disorder (ADHD) years before they are formally diagnosed, according to new research. ADHD is one of the most common mental disorders, affecting an estimated 8 percent of children and teenagers, with symptoms including trouble focusing, restlessness, and impulsivity. But many go undiagnosed for years, missing the chance for early support even when warning signs are already present. In a new study from Duke Health, researchers found that AI tools can analyse routine electronic health records to estimate a child's likelihood of developing ADHD well before a typical diagnosis. The findings, published in Nature Mental Health, suggest that patterns hidden in everyday medical data could help doctors identify children who may benefit from earlier evaluation and follow-up. "We have this incredibly rich source of information sitting in electronic health records," said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine. "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 researchers analysed health records from more than 140,000 children, both with and without ADHD, training an AI model to detect patterns from birth through early childhood. The system learned to recognise combinations of developmental, behavioural, and clinical events that often appeared years before an ADHD diagnosis. It proved highly accurate at estimating risk among children aged five and older, with consistent results across factors such as sex, race, ethnicity, and insurance status. Experts say earlier identification could lead to earlier diagnosis and support, which is linked to improved academic, social, and health outcomes for children with ADHD. "Children with ADHD can really struggle when their needs aren't understood and adequate supports are not in place," said Naomi Davis, an associate professor in the Department of Psychiatry and Behavioral Sciences and an author of the study. "Connecting families with timely, evidence-based interventions is essential for helping them achieve their goals and laying a foundation for future success." Researchers say that the tool is not designed to replace doctors or provide a complete diagnosis: "This is not an AI doctor," said Matthew Engelhard from Duke's Department of Biostatistics & Bioinformatics, and senior author of the study. He added: "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 team added that similar AI approaches are also being explored to better understand risks and causes of mental illness in adolescents. According to the NHS, common symptoms of ADHD in a child or young person include being easily distracted, struggling to listen, forgetting everyday tasks, and showing high levels of energy, such as fidgeting or tapping hands and feet. The disorder is also believed to be under-recognised in girls compared with boys, partly because girls are more likely to display inattentive symptoms, which can be harder to identify.

AI Tool May Spot ADHD Years Before Children Are Diagnosed | Newswise

Newswise -- 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 Health 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. "We have this incredibly rich source of information sitting in electronic health records," said Elliot Hill, lead author of the study and data scientist in the Department of Biostatistics & Bioinformatics at Duke University School of Medicine. "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." 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. The study was supported by grants from the National Institute of Mental Health (K01-MH127309, UL1 TR002553) and National Center for Advancing Translational Sciences.

AI Tool May Help Identify ADHD in Kids Long Before Typical Diagnosis

By HealthDay Staff HealthDay Reporter WEDNESDAY, April 29, 2026 (HealthDay News) -- Attention-deficit/hyperactivity disorder, or ADHD, affects millions of children, but many go years without a diagnosis, missing the chance for early support. Now, a new study from Duke Health, published April 27 in the journal Nature Mental Health, suggests artificial intelligence could help change that. "We have this incredibly rich source of information sitting in electronic health records," said lead author Elliot Hill, a data scientist at Duke University School of Medicine in Durham, North Carolina. "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." Hill and his colleagues created an AI model that estimated a child's risk of developing ADHD -- years before a typical diagnosis. The AI model reviewed medical data from more than 140,000 children, with and without ADHD, from birth through early childhood. It learned to spot combinations of developmental, behavioral and clinical signs that often appear long before diagnosis. The model proved highly accurate at estimating future ADHD in children age 5 and older. Results were consistent across sex, race, ethnicity and insurance status. Researchers say the tool can flag kids for earlier evaluation, diagnosis and support. This is important because early intervention is linked to better academic, social and health outcomes. "This is not an AI doctor," said senior author Dr. Matthew Engelhard, also from Duke University School of Medicine. "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." More information The National Institutes of Health has more information on ADHD. SOURCE: HealthDay TV, April 29, 2026