October 15, 2025

Yes, ADHD Diagnoses Are Rising, But That Doesn’t Mean It’s Overdiagnosed

Many news outlets have reported an increase – or surge – in attention-deficit/hyperactivity disorder, or ADHD, diagnoses in both children and adults. At the same time, health care providers, teachers and school systems have reported an uptick in requests for ADHD assessments.

These reports have led some experts and parents to wonder whether ADHD is being overdiagnosed and overtreated.

As researchers who have spent our careers studying neurodevelopmental disorders like ADHD, we are concerned that fears about widespread overdiagnosis are misplaced, perhaps based on a fundamental misunderstanding of the condition.

Understanding ADHD as a spectrum:

Discussions about overdiagnosis of ADHD imply that you either have it or you don’t.

However, when epidemiologists ask people in the general population about their symptoms of ADHD, some have a few symptoms, some have a moderate level, and a few have lots of symptoms. But there is no clear dividing line between those who are diagnosed with ADHD and those who are not, since ADHD – much like blood pressure – occurs on a spectrum.

Treating mild ADHD is similar to treating mild high blood pressure – it depends on the situation. Care can be helpful when a doctor considers the details of a person’s daily life and how much the symptoms are affecting them.

Not only can ADHD symptoms be very different from person to person, but research shows that ADHD symptoms can change within an individual. For example, symptoms become more severe when the challenges of life increase.

ADHD symptoms fluctuate depending on many factors, including whether the person is at school or home, whether they have had enough sleep, if they are under a great deal of stress or if they are taking medications or other substances. Someone who has mild ADHD may not experience many symptoms while they are on vacation and well rested, for example, but they may have impairing symptoms if they have a demanding job or school schedule and have not gotten enough sleep. These people may need treatment for ADHD in certain situations but may do just fine without treatment in other situations.

This is similar to what is seen in conditions like high blood pressure, which can change from day to day or from month to month, depending on a person’s diet, stress level and many other factors.

Can ADHD symptoms change over time?

ADHD symptoms start in early childhood and typically are at their worst in mid-to late childhood. Thus, the average age of diagnosis is between 9 and 12 years old. This age is also the time when children are transitioning from elementary school to middle school and may also be experiencing changes in their environment that make their symptoms worse.

Classes can be more challenging beginning around fifth grade than in earlier grades. In addition, the transition to middle school typically means that children move from having all their subjects taught by one teacher in a single classroom to having to change classrooms with a different teacher for each class. These changes can exacerbate symptoms that were previously well-controlled. Symptoms can also wax and wane throughout life.

Psychiatric problems that often co-occur with ADHD, such as anxiety or depression, can worsen ADHD symptoms that are already present. These conditions can also mimic ADHD symptoms, making it difficult to know which to treat. High levels of stress leading to poorer sleep, and increased demands at work or school, can also exacerbate or cause ADHD-like symptoms.

Finally, the use of some substances, such as marijuana or sedatives, can worsen, or even cause, ADHD symptoms. In addition to making symptoms worse in someone who already has an ADHD diagnosis, these factors can also push someone who has mild symptoms into full-blown ADHD, at least for a short time.

The reverse is also true: Symptoms of ADHD can be minimized or reversed in people who do not meet full diagnostic criteria once the external cause is removed.

How prevalence is determined:

Clinicians diagnose ADHD based on symptoms of inattention, hyperactivity and impulsivity. To make an ADHD diagnosis in children, six or more symptoms in at least one of these three categories must be present. For adults, five or more symptoms are required, but they must begin in childhood. For all ages, the symptoms must cause serious problems in at least two areas of life, such as home, school or work.

Current estimates show that the strict prevalence of ADHD is about 5% in children. In young adults, the figure drops to 3%, and it is less than 1% after age 60. Researchers use the term “strict prevalence” to mean the percentage of people who meet all of the criteria for ADHD based on epidemiological studies. It is an important number because it provides clinicians and scientists with an estimate on how many people are expected to have ADHD in a given group of people.

In contrast, the “diagnosed prevalence” is the percentage of people who have been diagnosed with ADHD based on real-world assessments by health care professionals. The diagnosed prevalence in the U.S. and Canada ranges from 7.5% to 11.1% in children under age 18. These rates are quite a bit higher than the strict prevalence of 5%.

Some researchers claim that the difference between the diagnosed prevalence and the strict prevalence means that ADHD is overdiagnosed.

We disagree. In clinical practice, the diagnostic rules allow a patient to be diagnosed with ADHD if they have most of the symptoms that cause distress, impairment or both, even when they don’t meet the full criteria. And much evidence shows that increases in the diagnostic prevalence can be attributed to diagnosing milder cases that may have been missed previously. The validity of these mild diagnoses is well-documented.

Consider children who have five inattentive symptoms and five hyperactive-impulsive symptoms. These children would not meet strict diagnostic criteria for ADHD even though they clearly have a lot of ADHD symptoms. But in clinical practice, these children would be diagnosed with ADHD if they had marked distress, disability or both because of their symptoms – in other words, if the symptoms were interfering substantially with their everyday lives.

So it makes sense that the diagnosed prevalence of ADHD is substantially higher than the strict prevalence.

Implications for patients, parents and clinicians:

People who are concerned about overdiagnosis commonly worry that people are taking medications they don’t need or that they are diverting resources away from those who need it more. Other concerns are that people may experience side effects from the medications, or that they may be stigmatized by a diagnosis.

Those concerns are important. However, there is strong evidence that underdiagnosis and undertreatment of ADHD lead to serious negative outcomes in school, work, mental health and quality of life.

In other words, the risks of not treating ADHD are well-established. In contrast, the potential harms of overdiagnosis remain largely unproven.

It is important to consider how to manage the growing number of milder cases, however. Research suggests that children and adults with less severe ADHD symptoms may benefit less from medication than those with more severe symptoms.

This raises an important question: How much benefit is enough to justify treatment? These are decisions best made in conversations between clinicians, patients and caregivers.

Because ADHD symptoms can shift with age, stress, environment and other life circumstances, treatment needs to be flexible. For some, simple adjustments like classroom seating changes, better sleep or reduced stress may be enough. For others, medication, behavior therapy, or a combination of these interventions may be necessary. The key is a personalized approach that adapts as patients’ needs evolve over time.

https://theconversation.com/yes-adhd-diagnoses-are-rising-but-that-doesnt-mean-its-overdiagnosed-257108?utm_medium=article_clipboard_share&utm_source=theconversation.com

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Variations in Diagnosis

Variations in Diagnosis

A cohort study looked at over five million adults, and over 850,000 children between the ages of five and eleven, who received care at Kaiser Permanente Northern California during the ten-year period from the beginning of 2007 through the end of 2016. At any given time, KPNC serves roughly four million persons. It is representative of the population of the region, except for the highest and lowest income strata.

Among adults rates of ADHD diagnosis rose from 0.43% to 0.96%. Among children the diagnosis rates rose from 2.96% to 3.74%, ending up almost four times as high as for adults.

Non-Hispanic whites had the highest adult rates throughout, increasing from 0.67% in 2007 to 1.42% in 2016. American Indian or Alaska Native (AIAN) had the second highest rates, rising from 0.56% to 1.14%. Blacks and Hispanics had roughly comparable rates of diagnosis, the former rising from 0.22% to 0.69%, the latter from 0.25% to 0.65%. The lowest rates were among Asians (rising from 0.11% to 0.35%) and Native Hawaiian or other Pacific Islanders (increasing from 0.11% to 0.39%).

Odds of diagnosis dropped steeply with age among adults. Relative to 18-24-year-olds, 25-34-year-olds were 1/6th less likely to be diagnosed with ADHD, 35-44-year-olds 1/3rd less likely, 45-54-year-olds less than half as likely, 55-64-year-olds less than a quarter as likely, and those over 65 about a twentieth as likely. This is consistent with other studies reporting and age dependent decline in the diagnosis.

Adults with the highest levels of education were twice as likely to be diagnosed as those with the lowest levels. But variations in median household income had almost no effect. Women were marginally less likely to be diagnosed than men.

ADHD is associated with some other psychiatric disorders. Compared with normally developing adults, and adjusted for confounders, those with ADHD were five times as likely to have an eating disorder, over four times as likely to be diagnosed with bipolar disorder or depression, more than twice as likely to suffer from anxiety, but only slightly more likely to abuse drugs or alcohol.

The authors speculate that rising rates of diagnosis could reflect increasing recognition of ADHD in adults by physicians and other clinicians as well as growing public awareness of ADHD during the decade under study. Turning to the strong differences among ethnicities, they note, Racial/ethnic differences could also reflect differential rates of treatment seeking or access to care. Racial/ethnic background is known to play an important role in opinions on mental health services, health care utilization, and physician preferences. In addition, rates of diagnosis- seeking to obtain stimulant medication for nonmedical use may be more common among white vs nonwhite patients. They conclude, greater consideration must be placed on cultural influences on health care seeking and delivery, along with an increased understanding of the various social, psychological, and biological differences among races/ethnicities as well as culturally sensitive approaches to identify and treat ADHD in the total population.

But the main take home message of this work is that most cases of ADHD in adults are not being diagnosed by clinicians. We know from population studies, worldwide, that about three percent of adults suffer from the disorder. This study found that less than 1 percent are diagnosed by their doctors. Clearly, more education is needed to teach clinicians how to identify, diagnose and treat ADHD in adults.

December 18, 2023

The Goal of ADHD Diagnosis? Safe and Effective Treatment

The Goal of ADHD Diagnosis? Safe and Effective Treatment

The diagnosis of ADHD should only be done by a licensed clinician, and that clinician should have one goal in mind: to plan a safe and effective course of evidence-based treatment. The infographic gives a summary of this diagnostic approach which my colleagues and I prepared for our "Primer" about ADHD,http://rdcu.be/gYyV.  A key point that parents of ADHD youth and adults with ADHD should keep in mind is that there is only one way to diagnose ADHD.An expert clinician must document the criteria for the disorder as specified by either the Diagnostic and Statistical Manual of the American Psychiatric Association, which is now in its fifth edition (DSM-5), or the World Health Organization's International Classification of Diseases (ICD-10). The two sets of criteria are nearly identical. These criteria are most commonly applied by a clinician asking questions of the parent (for children) and/or patient (for adolescents and adults).For children, information from the teacher can be useful. Some clinicians get this information by having the parent ask the teacher to fill out a rating scale. This information can be very useful if it is available.  When diagnosing adults, it is also useful to collect information from a significant other, which can be a parent for young adults or a spouse for older adults. But when such informants are not available, diagnosing ADHD based on the patient's self-report is valid. As the infographic indicates, any diagnosis of ADHD should also assess for comorbid psychiatric disorders, as these have implications for which ADHD medications will be safe and effective. And because a prior history of cardiovascular disease or seizures frequently contraindicate stimulants. These must also be assessed.

April 9, 2021

CDC: ADHD Diagnosis, Treatment, and Telehealth Use in Adults

The report "Attention-Deficit/Hyperactivity Disorder Diagnosis, Treatment, and Telehealth Use in Adults" published in the CDC's Morbidity and Mortality Weekly Report provides a detailed examination of the prevalence and treatment of ADHD among U.S. adults based on data collected by the National Center for Health Statistics Rapid Surveys System during October–November 2023. This data is crucial as it offers updated estimates on the prevalence of ADHD in adults, a condition often regarded as primarily affecting children, and highlights the ongoing challenges in accessing ADHD-related treatments, including telehealth services and medication availability.

Methods:

The methods used in this study involved the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS), which gathers data to approximate the national representation of U.S. adults through two commercial survey panels: the AmeriSpeak Panel from NORC at the University of Chicago and Ipsos’s KnowledgePanel. The data were collected via online and telephone interviews from 7,046 adults. The responses were weighted to reflect the total U.S. adult population, ensuring that the results approximate national estimates. In identifying adults with current ADHD, respondents were asked if they had ever been diagnosed with ADHD and, if so, whether they currently had the condition. The study also collected data on treatment types (including stimulant and nonstimulant medications), telehealth use, and demographic variables such as age, education, race, and household income.

Results:

The results showed that approximately 6.0% of U.S. adults, or an estimated 15.5 million people, had a current ADHD diagnosis. Notably, more than half of the adults with ADHD reported receiving their diagnosis during adulthood (age ≥18 years), indicating that diagnosis can occur well beyond childhood. Analysis of demographics showed significant differences between adults with ADHD and those without; adults with ADHD were more likely to be younger, with 84.5% under the age of 50. Adults with ADHD were also less likely to have completed a bachelor's degree and more likely to have a household income below the federal poverty level compared to those without ADHD. Regarding treatment, the report found that approximately one-third of adults with ADHD were untreated, and around one-third received both medication and behavioral treatment. Among those receiving pharmacological treatment, 33.4% used stimulant medications, and 71.5% of these individuals reported difficulties in getting their prescriptions filled due to medication unavailability, reflecting recent stimulant shortages in the United States. Additionally, nearly half of adults with ADHD had used telehealth services for ADHD-related care, including obtaining prescriptions and receiving counseling or therapy.

The discussion emphasizes the public health implications of these findings. ADHD is often diagnosed late, with many individuals not receiving a diagnosis until adulthood, which underscores the need for improved awareness and early identification of ADHD symptoms across the life course. Moreover, the high prevalence of untreated ADHD and the barriers to accessing stimulant medications reveal significant gaps in the healthcare system's ability to support adults with ADHD. These gaps can contribute to poorer outcomes, such as increased risk of injury, substance use, and social impairment. The report also highlights the role of telehealth, which became more prominent during the COVID-19 pandemic. Telehealth appears to provide a viable solution for expanding access to ADHD diagnosis and treatment, though challenges remain regarding the quality of care and potential for misuse. The authors suggest that improved clinical care guidelines for adults with ADHD could help reduce delays in diagnosis and treatment access, thus improving long-term outcomes for affected individuals.

Conclusion:

In conclusion, the study provides a comprehensive view of the prevalence, treatment, and telehealth use for ADHD among adults in the U.S.  These data are crucial for guiding clinical care and shaping policies related to medication access and telehealth services. The findings underscore the importance of ensuring an adequate supply of stimulant medications and reducing barriers to ADHD care, ultimately enhancing the quality of life for adults with this condition.   The good news is that many adults with ADHD are being diagnosed and treated.  It is, however, concerning that many are not treated and that many of those treated with stimulants were impacted by the stimulant shortage.

For more details, see:   https://www.cdc.gov/mmwr/volumes/73/wr/mm7340a1.htm

October 14, 2024

Brain Stimulation Therapy Shows No Benefit for ADHD in New Meta-analysis

ADHD is a neurodevelopmental condition rooted in delayed or atypical maturation of the prefrontal cortex  (the brain region that governs self-regulation). This maturational lag underlies the hallmark difficulties with attention, hyperactivity, and impulsivity, and also impairs what researchers call executive function: the cognitive toolkit we rely on for working memory, impulse control, mental flexibility, emotional regulation, and the ability to tolerate delays in reward. 

The Background:

Standard treatments work through two main routes. Stimulant and non-stimulant medications are considered very safe and effective treatments, but are not without risk of side effects and are not appropriate for every ADHD patient. Behavioral and psychosocial interventions can improve self-regulation and social functioning, but they require sustained effort and produce variable results. These limitations have kept the search for better alternatives active. 

One candidate that has drawn growing attention is transcranial direct current stimulation (tDCS). The technique is appealingly simple: a weak electrical current is applied to the scalp through small electrodes, modulating the excitability of neurons in the underlying cortex without requiring surgery, anesthesia, or significant discomfort. Its safety profile and ease of use have made it attractive to researchers. 

The Study: 

A newly published meta-analysis set out to give the technique its most rigorous test yet, pooling results from randomized controlled trials, including crossover designs, that compared active tDCS against sham stimulation in people with ADHD across all age groups. 

The Results: 

The findings were consistently null. Across seven trials enrolling 303 participants, tDCS produced no significant reduction in overall ADHD symptom severity compared with sham. Breaking symptoms into their components made no difference: neither hyperactivity/impulsivity nor inattention improved. Turning to executive function, 18 studies with 872 participants found no meaningful gain in inhibitory control, and 12 studies with 506 participants found the same for working memory. Smaller bodies of evidence, including three studies on cognitive flexibility (122 participants) and two on hot executive function, the motivational and emotional dimension of self-regulation (86 participants),  similarly came up empty. Variation in outcomes across studies was small to moderate, and there was no evidence of publication bias skewing the picture. 

The authors’ conclusion was succinct: tDCS was well tolerated but “did not demonstrate significant overall efficacy for core ADHD symptoms or executive functions.” 

July 2, 2026

Children and Adolescents with ADHD Face Significantly Higher Risk of Disordered Eating, Large U.S. Study Finds

Disordered eating (a broad category of persistent, harmful patterns in eating or weight control) affects between 5% and 22% of children and adolescents worldwide, with similar rates seen in the United States. The consequences are far-reaching: these conditions are linked to bone fractures, anemia, malnutrition, dental erosion, obesity, diabetes, hypertension, and elevated cholesterol and triglycerides. They also carry one of the highest mortality rates of any psychiatric illness. 

Eating disorders rarely occur in isolation. They frequently arise alongside other psychiatric and neurological conditions. Yet, until now, no large-scale study had examined these co-occurrences in a nationally representative U.S. sample. A new study addresses that gap, focusing on children and adolescents aged 6–17 and the conditions most commonly associated with disordered eating, including ADHD. 

The Study: 

Researchers drew on data from the 2022–2023 National Survey of Children's Health (NSCH), a nationally representative, cross-sectional survey covering all 50 states and Washington, D.C. Households were selected using stratified, address-based sampling, and parents or guardians completed surveys about one randomly selected child per household. The final sample included 68,000 children and adolescents. 

Results: 

After accounting for factors including sex, age, race and ethnicity, household income, educational attainment, insurance status, and household language, children and adolescents with ADHD were 2.6 times more likely to have some form of disordered eating compared to their typically developing peers. 

The elevated risk appeared across a range of specific behaviors: 

  • 60% more likely to over-exercise 
  • Twice as likely to experience a fear of vomiting or choking 
  • 2.4 times more likely to be extremely selective eaters, to skip meals, or to fast 
  • 2.7 times more likely to purge food or vomit 
  • 3 times more likely to show little interest in food 
  • 3.2 times more likely to binge eat 

A greater tendency toward using diet pills, laxatives, or diuretics was also observed in the ADHD group, though this finding did not reach statistical significance. 

The Take-Away: 

These findings underscore a need to improve both prevention and treatment strategies for disordered eating, particularly in children and adolescents who have ADHD. Clinicians working with this population are advised to screen for a wide spectrum of disordered eating behaviors.

The Retina as a Mirror: Decoding the ADHD AI "Breakthrough" and Its Fatal Flaws

The Background:

For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions. 

Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of  96.9%!

In the world of medical testing, scientists use a metric called  AUROC  (Area Under the Receiver Operating Characteristic) to measure how well a test works.

  • 0.5  means the test is no better than a coin flip (pure luck).
  • 1.0  represents a perfect test with zero mistakes. 

An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.

The Promise: How the AI "Sees" ADHD

To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.

On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.

Flaw #1: Batch Effects

The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:

  • The ADHD Group:  323 children recruited prospectively in a tight 6-month window in  2022 .
  • The Control Group:  323 children gathered retrospectively over a  17-year span  (2007 to 2024).This discrepancy triggers severe Batch Effects. This is a term scientists use to describe non-biological factors in an experiment that can cause inaccuracies in the data it produces. Fundus photography technology changed dramatically between 2007 and 2024. An investigation into the hardware uncovered shifts in camera models, lens optics, sensor degradation, and digital compression formats .Think of it this way: if you compare a selfie taken on the original 2007 iPhone with one from an iPhone 16, the AI doesn't need to look at your face to tell them apart; it just looks at the  2007 sensor noise  and pixel grain. The AI likely didn't learn to identify ADHD so much as it learned to distinguish between "old camera" and "new camera."

Flaw #2: Control Group

A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children. 

In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:

  • Refractive Errors (Myopia/Nearsightedness):  Severe myopia physically stretches the retina. This stretching alters vessel density and optic disc size, which were the exact markers the AI was examining.
  • Strabismus:  Misaligned eyes.
  • Ocular Anomalies:  Physical eye defects.Because these conditions directly alter retinal architecture, the AI likely learned to distinguish between "kids with ADHD" and "kids with severe eye problems," rather than "kids with ADHD" and "typical kids."

Fatal Flaw #3: The "Mirror Image" Leakage

When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as  Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant. 

Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics. 

The True Test: Differential Diagnosis 

The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.

When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor  63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.

Conclusion:

To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :

  1. Prospective, Unified Hardware:  Data must be collected on identical camera systems with the same protocols to eliminate technical "batch effects."
  2. Healthy, Community-Based Controls:  Comparisons must be made against truly "typically developing" children, not patients from eye clinics with their own retinal anomalies.
  3. Rigorous External Validation:  AI models must be tested on independent datasets from entirely different hospital networks to ensure they aren't just "memorizing" one hospital's specific machinery.Artificial Intelligence holds immense potential, but we must demand detective-like scrutiny before these tools reach our children. In the search for the "window to the mind," we have to make sure we aren't just looking at a smudge on the glass.

The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines. 

June 17, 2026