Humans aren’t great at identifying ADHD. But AI is

The barriers to getting a formal ADHD diagnosis are many: cost, time, the availability of qualified clinicians, a general lack of awareness. Plus, those with inattentive ADHD—which is believed to be more common in women and girls—often struggle with completing long or complex tasks, ironically making it extra difficult to go through the lengthy process of getting diagnosed. A further complication is the fact that ADHD doesn’t show itself in ways that can be objectively observed on a brain scan, X-ray or MRI. Instead, most cases are diagnosed by medical professionals through clinical interviews or standardized questionnaires. Despite affecting an estimated 7.2% of the population, including 11.4% of all children, an estimated 80% of those with ADHD never receive a formal diagnosis. “It usually comes down to a judgment call from a specialist provider,” says Elliot Hill, a biostatistician in the Department of Family Medicine at Duke University. “There’s no, ‘yes, you definitely have ADHD,’ or ‘you definitely have autism, or schizophrenia.’ The brain is too complicated, and our understanding is limited.” Studies also show that living with undiagnosed ADHD can have a wide range of negative outcomes, including challenges in academic and professional environments, due to distractibility, impulsivity, hyperactivity, poor memory, and deficient time management skills. Without proper coping strategies, treatment or medication, those with ADHD tend to be underemployed and have significantly lower lifetime earnings. They also suffer from higher rates of divorce, car accidents, substance abuse, unplanned pregnancies, eating disorders—even suicide attempts. Artificial intelligence, however, is quickly proving itself a game changer in ADHD diagnosis. The technology can spot patterns in healthcare records and flag those who may be at risk. Though clinicians are still a necessary part of the process, tools that search for symptoms and alert patients or parents are already provin