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November 16, 2023
The US Center for Disease Control's (CDC)review of ADHD starts with the statement: "Attention-deficit/hyperactivity disorder (ADHD) is a serious public health problem affecting many children and adults" (http://www.cdc.gov/ncbddd/adhd/research.html). My colleagues and I recently reviewed the ADHD literature. That let us describe ADHD as "... a seriously impairing, often persistent neurobiological disorder of high prevalence..." (Faraone et al., 2015). The figure 1, which comes from that paper, provides an overview of the lifetime trajectory of ADHD-associated morbidity.
Especially compelling data about ADHD and injuries comes from a recent paper, in Lancet Psychiatry, which used the Danish national registers to follow a cohort of 710,120 children (Dalsgaard et al., 2015a). Compared with children not having ADHD, those with ADHD were 30% more likely to sustain injuries than other children. Pharmacotherapy for ADHD reduced the risk for injuries by 32% from 5 to 10 years of age. Pharmacotherapy for ADHD reduced emergency room visits by 28.2% at age 10and 45.7% at age 12.
These results are shown in Figure 2, taken from the publication.
Especially compelling data about ADHD and injuries comes from a recent paper, in Lancet Psychiatry, which used the Danish national registers to follow a cohort of 710,120 children (Dalsgaard et al., 2015a). Compared with children not having ADHD, those with ADHD were 30% more likely to sustain injuries than other children. Pharmacotherapy for ADHD reduced the risk for injuries by 32% from 5 to 10 years of age. Pharmacotherapy for ADHD reduced emergency room visits by 28.2%at age 10and 45.7% at age 12.
These results are shown in Figure2, taken from the publication. The Figure compares the prevalence of injuries among three groups. ADHD children treated with medication, ADHD children not treated with medication, and children without ADHD. The Figure shows how ADHD risk for injuries occurs for all age groups. It also shows how the risk for injuries drops with treatment so that by age 12, the prevalence of injuries among treated ADHD children is the same as the prevalence of injuries for children without ADHD.
Documented examples of ADHD-associated injuries which impact day-to-day functioning include severe burns (Fritz and Butz, 2007), dental injuries (Sabuncuoglu, 2007), penetrating eye injuries (Bayar et al., 2015), the hospital treated injuries (Hurtig et al., 2013), and head injuries (DiScala et al., 1998). In one study (DiScala et al., 1998), when compared to other children admitted to the hospital for injuries, ADHD children were more likely to sustain injuries in multiple body regions (57.1% vs 43%), sustain head injuries (53% vs 41%), and to be severely injured as measured by the Injury Severity Score (12.5% vs5.4%) and the Glasgow Coma Scale (7.5% vs 3.4%).
Injuries are a substantial cause of ADHD-associated premature death. This assertion comes from the work of Dalsgaard et al. (2015b)based on the same Danish registry discussed above. In this second study, ADHD was associated with an increased risk for premature death and 53% of those deaths were due to injuries. They reported the risk for premature death in three age groups: 1-5, 6-17, and >17. For all three age groups, they found a greater risk for death in the ADHD group. For ages 6 to 17 and greater than 17. The ADHD-associated risk for mortality remained significant after excluding individuals with antisocial or substance use disorders.
There are currently no data about the effect of ADHD treatment on ADHD-associated premature death. We do, however, know from the data reviewed above that ADHD treatment reduces injuries and that half the deaths in the ADHD group were due to injuries. From this, we infer that ADHD treatments could reduce the risk of ADHD-associated premature death.
Two other ADHD-associated mobilities, obesity and cigarette smoking, have clear medical consequences. In a meta-analysis of 42 cross-sectional studies comprising 48,161 people with ADHD and 679,975 controls, my colleagues and I reported that the pooled prevalence of obesity was increased by about 40% in ADHD children compared with non-ADHD children and by about 70% in ADHD adults compared with non-ADHD adults(Cortese et al.,2015). The association between ADHD and obesity was significant for ADHD medication-naïve subjects but not for those medicated for ADHD, which suggests that medication reduces the risk for obesity.
Likewise, a meta-analysis of 27 longitudinal studies assessed the risk for several addictive disorders with sample sizes ranging from 4142 to 4175 for ADHD and 6835 to 6880 for non-ADHD controls (Lee et al., 2011). Children with ADHD were at higher risk for disorders of abuse or dependence on nicotine, alcohol, marijuana, cocaine, and other unspecified substances. Another meta-analysis (42 studies totaling, 2360 participants) showed that medications for ADHD reduced the ADHD-associated risk for smoking (Schoenfelder et al., 2014). The authors concluded that, for ADHD patients, "Consistent stimulant treatment for ADHD may reduce the risk of smoking". This finding is especially notable given that, for ADHD youth, cigarette smoking is a gateway drug to more serious addictions (Biederman et al., 2006).
Yes, ADHD is a serious disorder. Although most ADHD people will be spared the worst of these outcomes, they must be considered by parents and patients when weighing the pros and cons of treatment options.
Bayar, H., Coskun, E., Oner, V., Gokcen,C., Aksoy, U., Okumus, S. & Erbagci, I. (2015). Association between penetrating eye injuries and attention deficit hyperactivity disorder in children.Br J Ophthalmol99, 1109-11.
Biederman, J., Monuteaux, M., Mick, E., Wilens, T., Fontanella, J.,Poetzl, K. M., Kirk, T., Masse, J. & Faraone, S. V. (2006). Is cigarette smoking a gateway drug to subsequent alcohol and illicit drug use disorders? A controlled study of youths with and without ADHD. Biol Psychiatry59, 258-64.
Cortese, S., Moreira-Maia, C. R., St Fleur, D., Morcillo-Penalver, C.,Rohde, L. A. & Faraone, S. V. (2015). Association Between ADHD and Obesity: A Systematic Review and Meta-Analysis. Am J Psychiatry, appiajp201515020266.
Dalsgaard, S., Leckman, J. F., Mortensen, P. B., Nielsen, H. S. &Simonsen, M. (2015a). Effect of drugs on the risk of injuries in children with attention deficit hyperactivity disorder: a prospective cohort study. Lancet Psychiatry2, 702-9.
Dalsgaard, S., Ostergaard, S. D., Leckman, J. F., Mortensen, P. B.& Pedersen, M. G. (2015b). Mortality in children, adolescents, and adults with attention deficit hyperactivity disorder: a nationwide cohortstudy. Lancet385, 2190-6.
DiScala, C., Lescohier, I., Barthel, M. & Li, G. (1998).Injuries to children with attention deficit hyperactivity disorder. Pediatrics102, 1415-21.
Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J.,Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. J. S.,Tannock, R. & Franke, B. (2015). Attention deficit hyperactivitydisorder. In Nature Reviews: DiseasePrimers.
Fritz, K. M. & Butz, C. (2007). Attention Deficit/Hyperactivity Disorder and pediatric burn injury: important considerations regarding premorbid risk. Curr Opin Pediatr19, 565-9.
Hurtig, T., Ebeling, H., Jokelainen, J., Koivumaa-Honkanen, H. &Taanila, A. (2013). The Association Between Hospital-Treated Injuries and ADHD Symptoms in Childhood and Adolescence: A Follow-Up Study in the Northern Finland Birth Cohort 1986. J Atten Disord.
Lee, S. S., Humphreys, K. L., Flory, K., Liu, R. & Glass, K. (2011).Prospective association of childhood attention-deficit/hyperactivity disorder(ADHD) and substance use and abuse/dependence: a meta-analytic review. Clin Psychol Rev31, 328-41.
Sabuncuoglu, O. (2007). Traumatic dental injuries and attention-deficit/hyperactivity disorder: is there a link? Dent Traumatol23,137-42.
Schoenfelder, E. N., Faraone, S. V. & Kollins, S. H. (2014).Stimulant treatment of ADHD and cigarette smoking: a meta-analysis. Pediatrics133, 1070-1080.
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.
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.
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.
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:
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:
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 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.
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 :
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.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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