=Paper=
{{Paper
|id=Vol-3751/paper1
|storemode=property
|title=Artificial Intelligence in ADHD treatment: a brief narrative review
|pdfUrl=https://ceur-ws.org/Vol-3751/paper1.pdf
|volume=Vol-3751
|authors=Alessandro Frolli,Emilio Saviano,Maria Carla Ricci,Simona Luigia Sica,Marco Romano
|dblpUrl=https://dblp.org/rec/conf/dilend/FrolliSRS024
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==Artificial Intelligence in ADHD treatment: a brief narrative review==
https://ceur-ws.org/Vol-3751/paper1.pdf
Artificial Intelligence in ADHD treatment: a brief
narrative review
Alessandro Frolli1, Emilio Saviano2, MariaCarla Ricci3, Simona Luigia Sica4, Marco Romano1
1
Department of International Humanities and Social Sciences, Rome University of International Studies, Rome (Italy)
2
Healt Psychology and Science Communication, San Raffaele Telematic University of Rome, Rome (Italy)
3
Fondazione Italiana Neuroscienze e Disordini dello Sviluppo, Caserta (Italy)
4
Department of Humanities, University of Naples “Federico II”, Napoli, (Italy)
Abstract
Artificial intelligence (AI) is revolutionizing the treatment of Attention Deficit
Hyperactivity Disorder (ADHD) through more precise diagnoses and personalized
therapies. AI-based cognitive training programs utilize technologies such as
augmented reality (AR) and virtual reality (VR) to enhance executive functions in
children with ADHD, offering interactive and personalized exercises that stimulate
attention and impulse control. These programs, including CogniFit, BrainBeat,
NeuroPlus, and Lumosity, integrate real-time feedback and continuous monitoring,
increasing motivation and treatment effectiveness. AI supports early diagnosis by
analyzing data from neuropsychological, behavioral, and neurophysiological tests,
identifying complex patterns associated with ADHD. Examples of diagnostic tools
include QbTest, Adeo, NeuroElectrics, Mindstrong Health, and Cortica. However,
despite promising results, it is necessary to scientifically validate these approaches
and address challenges related to accessibility and data privacy protection. In
conclusion, AI represents an innovative frontier in ADHD treatment, significantly
improving patients' quality of life through personalized therapies and dynamic
monitoring.
Keywords
ADHD, IA, Virtual Reality, Serious Games, Attention, Adolescents, Children, eHealt
1. Introduction
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that profoundly
impacts the lives of children and adults worldwide. This disorder is characterized by a persistent
pattern of inattention, hyperactivity, and impulsivity, which significantly impairs the adaptive and
daily functioning of affected individuals [1]. ADHD not only affects concentration and motor
behavior but also involves deficits in executive functions, emotion regulation and modulation, and
motivation [2]. These symptoms make it difficult for individuals with ADHD to manage the
demands of school, work, and social life. The use of artificial intelligence (AI) for individuals
diagnosed with ADHD represents an area of growing interest and innovation. AI offers numerous
applications ranging from early diagnosis to the management and treatment of the disorder,
providing personalized and efficient solutions [3]. In this brief review, we aim to introduce and
support the use of artificial intelligence for the diagnosis and treatment of ADHD. The history of AI
in the treatment of ADHD is recent but rapidly evolving, combining
________________________________________________________
Proceedings of the Digital Innovations for Learning and Neurodevelopmental Disorders, May24–25, 2024, Rome, Italy
alessandro.frolli@unint.eu (A. Frolli); emilio.saviano@uniroma5.it (E. Saviano); mariacarla.ricci1@gmail.com (M.C.
Ricci); lusisica@unina.it (L.S. Sica); ); marco.romano@unint.eu (M. Romano);
0000-0002-2784-2887(A. Frolli); 0009-0006-6553-702X(E.Saviano); 0000-0001-5712-2939 (M.C. Ricci); 0000- 0001-5587-
8097 (L. Sica); 0000-0002-8581-3160 (M. Romano);
© 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
advanced technologies with innovative therapeutic approaches to improve the diagnosis, treatment,
and monitoring of this disorder. Since the 2000s, eHealth has begun to be used in clinical settings
for various disorders [4]. The initial applications mainly involved diagnostic support tools based on
machine learning algorithms that analyzed behavioral and
neuropsychological data to identify patterns associated with ADHD [5]. With the advancement of
machine learning and deep learning technologies [6], the algorithms have become more
sophisticated, allowing for greater accuracy in diagnosing and predicting ADHD symptoms. Natural
language processing (NLP) techniques and the analysis of large behavioral datasets are being used
to better understand patterns of attention and hyperactivity.
2. Mobile and Wearable Applications
The increase in the use of mobile and wearable devices has opened up possibilities for the
continuous monitoring of ADHD symptoms. AI-based mobile applications can monitor user
behavior and provide real-time feedback [7], while wearable devices collect physiological and
behavioral data for analysis [8]. Mobile applications and wearable devices represent one of the most
promising frontiers in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). These
tools combine the power of artificial intelligence with the practicality and pervasiveness of mobile
technologies to offer innovative solutions in monitoring, diagnosing, and treating ADHD [9;10;11].
By examining these technologies more closely, we describe some features of these Intelligent Tools
(IT):
Behavioral Monitoring and Data Collection: Mobile applications for ADHD treatment often
include behavioral monitoring features. These applications can record various aspects of a patient’s
daily behavior, such as physical activity levels, sleep patterns, and phone usage. The collected data
is analyzed using machine learning algorithms to identify patterns and provide real-time feedback
to both patients and doctors. For example, apps like Parenting Management allow parents to
monitor their children's behavior, recording instances of inattention or hyperactivity and providing
advice on how to manage them [12].
Games and Cognitive Training: Many mobile applications use games and cognitive training
activities designed to improve executive functions such as attention, working memory, and impulse
control [13;14]. These games are often personalized using AI to adapt to the child's skill level and
provide appropriate challenges. An example is Akili Interactive's EndeavorRx, a therapeutic video
game approved by the FDA for the treatment of ADHD in children [15]. The game uses attention
and multitasking tasks to enhance the cognitive abilities of children with ADHD.
Personalized Treatment Plans: Applications can also provide personalized treatment plans
based on the collected data. By analyzing behavioral data, these applications can suggest specific
interventions and adapt treatment strategies to the individual needs of the patient. For instance,
MyADHD is an application that offers tools for symptom monitoring and treatment management,
allowing patients, parents, and doctors to collaborate effectively [16].
Support for Behavioral Therapy: Some mobile applications are designed to support cognitive-
behavioral therapy (CBT) [17]. These applications can provide exercises, reminders, and feedback to
help patients develop coping skills and better manage ADHD symptoms. CBT-i Coach is an
example of an app that, although originally developed for insomnia, has been adapted to include
modules that help manage ADHD, offering relaxation techniques and problem-solving strategies.
Physiological Monitoring: Wearable devices, such as smartwatches and fitness trackers, can
constantly monitor physiological parameters like heart rate, physical activity levels, and sleep
patterns. This data is crucial for better understanding the patient's behavior and the factors
influencing ADHD symptoms [18]. For example, the Mente Autism Headset uses EEG sensors to
monitor brain activity and provide neurofeedback, which can help improve attention [19].
Biofeedback and Neurofeedback: Some wearable devices are designed to provide real-time
biofeedback or neurofeedback. These tools can help patients develop greater control over their
physiological and behavioral responses [20]. Muse is a headband device that uses EEG sensors to
monitor brain activity and provide feedback during guided meditation sessions, helping patients
improve concentration and reduce stress.
Reminders and Alerts: Smartwatches can be programmed to provide reminders and alerts that
help patients with ADHD stay organized and follow daily routines. Features like medication
reminders, schoolwork completion prompts, or scheduled activity participation can be extremely
helpful. For instance, Apple Watches can be configured with personalized reminders to support the
daily management of ADHD symptoms [21].
Applications Specific to Wearable Devices: Some applications are designed specifically for
wearable devices, leveraging the unique capabilities of these devices. For example, the Focus@Will
app uses music and sound to enhance concentration and productivity [22] and is compatible with
various wearable devices to provide a personalized experience.
3. AR and VR-Based Cognitive Training Programs
Several platforms offer AI-based cognitive training programs to improve executive functions in
children [23; 24]. These programs include games and activities designed to stimulate specific brain
areas associated with attention and impulse control. AI-based cognitive training programs are
emerging as effective tools in the treatment of Attention Deficit Hyperactivity Disorder (ADHD).
These programs aim to enhance the executive functions and cognitive abilities of patients through
interactive and personalized exercises. In this context, AR and VR technologies have begun to be
integrated into ADHD treatments [25], providing immersive environments for attention and self-
control exercises [26]. VR allows for the creation of highly controlled and personalized
environments where patients can be exposed to specific scenarios designed to improve certain
cognitive and behavioral skills. These environments can simulate real-life situations, such as a
classroom or home setting, offering a safe and risk-free context for practice and learning [27]. VR
also enables the simulation of real-life situations that patients may find difficult to manage. For
example, a patient can be exposed to a virtual classroom environment to practice attention and
behavior management techniques. This simulation provides a unique opportunity to practice in a
realistic yet controlled setting.
Initial Diagnosis and Assessment: Cognitive training programs often start with a diagnosis and
assessment phase, during which data on the patient’s cognitive and behavioral functioning is
collected [28]. This phase may include neuropsychological tests, questionnaires, and clinical
observations. AI analyzes this data to identify areas of weakness and specific patient needs.
Program Personalization: One of the main features of AI-based cognitive training programs is
their ability to personalize [29]. Using machine learning algorithms, the program continuously
adapts exercises and activities based on the patient's progress and responses. This approach ensures
that the training is always tailored to individual needs.
Interactive Exercises and Gamification: The proposed exercises are often presented as interactive
games, making the training more engaging and motivating for children. These games are designed
to stimulate specific cognitive functions, such as sustained attention, working memory, impulse
control, and planning ability. Gamification increases motivation and adherence to the program [30].
Feedback: AI-based programs offer immediate feedback on the patient’s progress, allowing
dynamic adjustment of activities. Continuous monitoring enables tracking of improvements over
time and identification of areas needing further attention.
Integration with Traditional Therapies: Many cognitive training programs are designed to be used
in combination with traditional therapies, such as cognitive-behavioral therapy (CBT) or
pharmacological therapy [31]. Integrating different treatment methodologies can enhance the
overall effectiveness of the therapy, granting a more impactful intervention.
Examples of AI-Based Cognitive Training Programs (see Tab.1):
1. CogniFit: CogniFit offers a range of AI-based cognitive training programs aimed at
improving various cognitive functions. CogniFit's games and activities are designed to be
fun and engaging, and patient progress is continuously monitored to adjust the difficulty
level.
2. Cognifit VR: Cognifit has developed a series of VR-based cognitive training exercises
designed to improve specific cognitive functions in patients with ADHD. The exercises
include interactive games that challenge working memory, sustained attention, and
planning skills.
3. BrainBeat: BrainBeat is a cognitive training program based on rhythms and music, designed
to improve attention and impulse control. It uses real-time feedback technologies to help
children develop sustained attention and synchronization skills [32].
4. NeuroPlus: NeuroPlus combines neurofeedback, cognitive training, and interactive games to
improve executive functions in children with ADHD. It uses EEG sensors to monitor brain
activity and provide immediate feedback, helping children improve self-regulation and
attention [33].
5. Lumosity: Lumosity [34] is a brain training application offering a wide range of games
designed to improve memory, attention, mental flexibility, and other cognitive functions.
Although not specifically developed for ADHD, many parents and therapists use it as a
complementary support in ADHD treatment.
6. Virtually Better: Virtually Better develops VR applications for the treatment of various
psychological disorders, including ADHD. Their applications use immersive environments
to provide personalized cognitive and behavioral training.
7. Mightier: Mightier uses biofeedback games to help children improve emotional control and
stress management [35]. Children wear a sensor that measures heart rate, and the game
adjusts in real-time to help the child practice relaxation techniques.
Table.1
AI-Based Cognitive Training Programs
App Program Device
Cognifit Contextualized Memory; Smartphone; PC; Tablet
Motor Coordination;
Planning; Working Memory;
Cognifit VR Contextualized Memory; Virtual Reality Headset
Motor Coordination;
Planning; Working Memory;
BrainBeat Attention; Arm Sensor; Metronome;
Behavioral Regulation; Headphones
Coordination
NeuroPlus Attention; Smartphone; PC; Tablet
Self Regulation
Lumosity Memory; Smartphone; PC; Tablet
Attention;
Cognitive Flexibility
Mightier Planning; Attention; Arm Sensor;Smartphone; PC;
Working Memory; Tablet
Behavioral Regulation;
The benefits of these programs include the ability to customize the program to the individual's
specific needs, accessibility to resources (especially free apps), maintaining the effectiveness of the
training program over time thanks to its ability to adapt based on the subject's responses, the
motivation that immersive environments can elicit, and the ability to analyze large amounts of data
[36]. This is made possible by machine learning algorithms that analyze patient data to personalize
and adapt the training, and by natural language processing (NLP) systems.
4. Early Diagnosis and Personalized Treatment
AI is emerging as a powerful and innovative tool to support the early diagnosis of ADHD, thanks to
its ability to analyze large amounts of data and identify complex patterns [37]. AI can integrate and
analyze data from various sources, including standardized tools such as the Conners' Rating Scale or
the Achenbach System of Empirically Based Assessment (ASEBA); behavioral data, collected
through direct observation or video recordings; neuropsychological data; neurophysiological data
such as EEG and electrocardiogram (ECG) readings; and information about the patient's genetic
heritage [38]. Integrating these data allows AI to provide a comprehensive and detailed view of the
patient, improving diagnostic accuracy. Machine learning algorithms are trained on large datasets
to identify patterns associated with ADHD [39]. These algorithms can recognize combinations of
symptoms and behaviors that might not be evident through traditional clinical observation. Some of
the methods used include: (a) Artificial Neural Networks: Models that mimic the functioning of the
human brain and can recognize complex patterns in data [40]; (b) Random Forests: Algorithms that
use a combination of decision trees to improve predictive accuracy; (c) Support Vector Machines
(SVM): Techniques that find the optimal boundary between different classes of data to classify
patients [41].
Examples of AI Applications in Early ADHD Diagnosis (see Tab.2):
QbTest: The QbTest [42] is a diagnostic system that combines continuous performance testing
(CPT) with motion analysis. It uses AI to compare patient data with a reference database and
provides an objective assessment of symptoms related to attention, impulsivity, and hyperactivity.
This test is used for both diagnosis and monitoring treatment effectiveness.
Adeo: Adeo is a mobile application that uses machine learning to analyze responses to behavioral
questionnaires and data from wearable sensors. The app provides an initial assessment of the risk of
ADHD and suggests possible interventions. It is designed for use by parents and teachers to
monitor at-risk children.
NeuroElectrics: NeuroElectrics develops portable EEG devices that can be used to monitor brain
activity [43]. The data collected is analyzed by AI algorithms to identify patterns associated with
ADHD. These devices are used for both early diagnosis and continuous monitoring of patients.
Mindstrong Health:Mindstrong Health uses AI to analyze behavioral data collected through
smartphone use, such as typing patterns and app usage [44]. The algorithms can identify changes in
behaviors that might indicate the presence of ADHD or other mental disorders. This technology
offers a non-invasive solution for continuous symptom monitoring.
Cortica:Cortica uses a combination of neuroimaging, genetics, and behavioral data to diagnose
and treat ADHD. AI analyzes these data to provide a comprehensive and personalized diagnostic
assessment, identifying ADHD subtypes and suggesting specific treatments.
These AI applications enable early and accurate diagnosis of ADHD by integrating and
analyzing diverse data sources, thus offering a personalized approach to treatment. The use of AI in
ADHD diagnosis and treatment represents a significant advancement in providing tailored and
effective care for patients.
Table 2
App Aim Device
QbTest ADHD diagnosis through Smartphone; PC; Tablet
online questionnaire
NeuroElectrics Treatment Protocol EEG; Transcranial Electrical
Stimulation
MindstrongHealt Detect biometric informations Smartphone
due the use of smatphone to
make early diagnosis
Cortica Treatment Protocol EEG; Cranial Electrotherapy
Stimulation Device
5. Benefits of Using Artificial Intelligence in the Treatment of ADHD
AI can enhance the diagnosis and assessment of ADHD through the analysis of large volumes of
clinical data. Machine learning algorithms can identify patterns in behaviors and cognitive tests that
may not be evident to humans [45]. AI can help create personalized treatment plans. By analyzing
individual data, algorithms can suggest specific interventions and continuously monitor the
patient's progress. AI applications, such as wearable devices and mobile apps, can provide
continuous monitoring and real-time feedback. This can help patients better manage ADHD
symptoms in their daily lives [46]. AI can reduce clinical bias in diagnoses and treatments, offering
a more objective assessment based on data rather than subjective impressions [47;48:49].
5.1. Limitations of Using Artificial Intelligence in the Treatment of ADHD
The effectiveness of AI algorithms depends on the quality and quantity of available data. Incomplete
or low-quality data can lead to incorrect diagnoses or ineffective treatments [50].The collection and
processing of sensitive data raise concerns about privacy and security. Patient data must be
protected from unauthorized access and potential breaches [51]. AI algorithms can be seen as "black
boxes," where the decision-making process is not transparent. This can make it difficult for doctors
and patients to understand and trust the recommendations provided by AI. Excessive reliance on AI
can lead to a reduction in traditional clinical skills. It is important to maintain a balance between the
use of AI and human diagnostic and therapeutic skills [52]. Not all patients have equal access to
advanced technologies. Socioeconomic disparities can limit access to AI, exacerbating inequalities in
mental health care [53]. One major limitation is the requirement for large, labeled datasets, which
can be difficult to obtain. Early-stage research often suffers from diagnostic ambiguity and small
sample sizes, which can hinder the training and validation of AI models. The complexity of ADHD
as a disorder, involving both behavioral and neurophysiological aspects, presents a challenge for AI
models. While models like CNNs can analyze neurophysiological data, they are sensitive to training
data specifics and require intricate preprocessing, which can introduce bias or loss of critical
information [54]. Translating AI-driven insights into clinically actionable treatment strategies
remains a challenge. Although AI can identify patterns and biomarkers, integrating these findings
into practical, everyday treatment for ADHD requires further research and validation [55].
6. Conclusions
In conclusion, AI is revolutionizing the treatment of ADHD through more precise diagnoses [56],
personalized therapies, and continuous monitoring, promising to significantly improve the quality
of life for people affected by this disorder. AI-based cognitive training programs represent an
innovative frontier in ADHD treatment, offering new opportunities to enhance the cognitive
abilities and executive functions of patients. However, it is essential to address challenges related to
efficacy, accessibility, and data protection to ensure that these tools can be used effectively and
safely. With continued technological progress and greater integration of AI technologies, the future
of ADHD treatment with cognitive training programs looks promising.
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