AI is hard to avoid. Everywhere you look, AI is being promoted as a cure for almost all human quirks. While many people may not be aware of it, AI is already a part of their day-to-day routines. AI impacts how we work, shop, learn, and make decisions. Because AI is a powerful tool for processing and analyzing large amounts of data, it is being applied to medical and mental health issues. For example, AI is now being used for the early detection and prevention of cancer (CRI Staff, 2025). AI’s use in mental health may be lagging a bit, but it’s only a matter of time before AI is routinely used in the diagnosis and treatment of ADHD and other childhood psychiatric disorders. At the very least, AI will be part of the future of ADHD diagnosis.
The potential of AI in ADHD diagnosis
Traditional methods of diagnosing ADHD vary greatly. The current processes for ADHD diagnosis often involve gathering a child’s family, educational, and biological history, their performance in school, behavior observations, and parent and teacher behavior rating forms. A thorough ADHD evaluation considers the degree of impairment that the ADHD symptoms are causing. Perhaps more importantly, it considers whether there might be alternative explanations for the child’s difficulties. This can be a time-consuming process. Even thorough evaluations usually do not provide observation of the child across settings, which is part of the diagnostic criteria noted in the DSM-V. Nor do many evaluations provide any neurophysiological data, such as an EEG or precise measurements of motor activity that might assist in diagnosing ADHD.
Many children diagnosed with ADHD are not afforded the opportunity to undergo extensive evaluations. Instead, many children are diagnosed by busy pediatricians who, due to the constraints of health insurance, may only have 10 to 15 minutes to meet with the child and then do a quick review of rating forms completed by parents and teachers.
However, AI may offer additional strategies for diagnosing and assessing ADHD for physicians as well as psychologists and clinicians who are asked to diagnose ADHD. Because AI can analyze vast amounts of multimodal data, including information collected in medical records, it can “identify complex patterns and biomarkers associated with ADHD” (Bongurala et al., 2024). AI can be used to combine information from many sources, including levels of physical activity, profiles from rating forms, and brain imaging to assist in diagnosing ADHD. Using AI to assess ADHD can lead to more comprehensive evaluations.
How AI Enhances ADHD Evaluation
AI can add science to our assessment of ADHD. Many clinicians and physicians can be quick to diagnose ADHD when a child walks or runs into our offices and leaps onto the nearest chair or couch. Ten seconds into the appointment, the diagnosis is made. AI tools show the potential to analyze video of a child engaged in specified tasks that can be compared to how others perform these activities (Li et al., 2024). In theory, a parent who collects videos of their child across situations and over developmental stages may provide dedicated AI programs with the information they need to diagnose ADHD.
AI can also be used to analyze a child’s responses to real-world scenarios in VR. Because VR can create consistent scenarios of school or home environments, it is an ideal tool to “observe” a child in situations where attention skills are needed. Using VR and AI for ADHD assessment has been employed for a few years (Stokes et al., 2022). Recent advances with a new device called the Virtual Classroom Attention Tracker utilizes AI to interpret all attention and motion activity observed within a VR classroom (Chen, 2025). This combination of using data from a VR environment along with AI provides a new set of information that can assist in the diagnosis of ADHD, analyze large amounts of behavioral data from VR, and compare a child to thousands of others for whom similar data has been collected (Zaheer & Akhtar, 2025).
AI’s Limitations and the Importance of Holistic ADHD Assessment
One of the innovative ways that AI might be used to diagnose ADHD is to monitor brain functioning. AI applications are being applied to brain imaging and brain activity monitoring, such as EEGs and ECGs to assess ADHD. While the jury is still out as to how effective monitoring these types of brain functions are in the diagnosis of ADHD, there appear to some characteristic patterns found in the brains of people diagnosed with ADHD. A recent review of 54 studies indicates that AI technologies were able to diagnose ADHD with accuracy between 70% to 95% (Zaheer & Akhtar, 2025). While AI might help in the identification of brain ADHD patterns, it provides limited information about the level and type of impairment or how much ADHD is interfering with functioning or development. AI could add to diagnostic clarity when it is applied to analyzing behavior and functional performance collected via behavior rating data provided by parents, teachers, self-reports. The benefits of AI for ADHD evaluation are becoming increasingly clear.
It’s clear that a combined approach that uses personal observations, psychological testing, and AI analysis of virtual reality and video resources could lead to better ADHD diagnoses. However, if the AI doesn’t have enough information, it might miss important factors that could affect ADHD symptoms. These factors include things like learning disabilities, trauma, autism, challenges with executive functioning, stress, medical problems, and other mental health conditions. AI in ADHD diagnosis for children requires careful consideration of all contributing factors.
For AI to be effective, it needs detailed information about a person’s background and experiences. This includes any stress or trauma they’ve faced, learning disabilities that make it hard for them to focus in class, family issues that distract them, and other mental health concerns like anxiety and depression that might affect their attention. It’s also important to consider any medical conditions that could impact a person’s ability to concentrate.
What AI Can and Cannot Do in ADHD Diagnosis
If you want to get a sense of how AI might help with diagnosing ADHD, try one out. Ask a popular AI model, such as OpenAI’s GPT-4, Gemini, or Perplexity, to diagnose your child’s ADHD. You will quickly learn that these AIs cannot and will not provide medical diagnoses. However, they can offer you the diagnostic criteria for ADHD. They may also discuss some of the confusing symptoms that parents often observe in their children with ADHD, such as how kids can play video games or build with Legos for hours but quickly lose focus on other mundane activities. The AI will consistently advise you to consult a professional. One way that AI can be helpful is by guiding you on how to collect relevant information for an assessment, including observations of your child’s behavioral difficulties, school reports, and insights into the level of impairment caused by their attention issues.
If you don’t already know it, AI is in your home and coming soon to your local mental health and medical providers’ offices. It will become a part of ADHD diagnosis. Currently, using AI to diagnose ADHD is best seen as a supplement to traditional assessment. However, as AI technology evolves, its role in ADHD diagnosis and treatment will likely become even more prominent. It will be incumbent upon professionals to incorporate this tool into their assessment toolbox.
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