Cognitive Fingerprinting of Attention-Deficit/Hyperactivity Disorder Subtypes via Artificial Intelligence-Driven Virtual Reality

Background: Attention-deficit/hyperactivity disorder ({ADHD}) is one of the most prevalent neurodevelopmental disorders, defined by persistent patterns of inattention, hyperactivity, and/or impulsivity that impair academic, social, and occupational functioning across the lifespan. Affecting approximately 5\% of children and adolescents worldwide, {ADHD} frequently persists into adulthood, contributing to long-term psychosocial difficulties.

Objective: To outline the limitations of current {ADHD} diagnostic approaches and highlight how immersive virtual reality ({VR}) may strengthen ecological relevance, subtype differentiation, and contextual sensitivity.

Methods: We reviewed the shortcomings of existing diagnostic tools, including their restricted ecological validity and limited ability to capture real-world attentional demands. We then examined immersive {VR} as a platform capable of standardized, realistic testing environments and simultaneous acquisition of multimodal behavioral and physiological data.

Results: Evidence suggests that {VR}-based assessments can enhance the precision of {ADHD} subtype profiling, support individualized behavioral characterization, and overcome several constraints of traditional diagnostic methods. Common {VR} paradigms and prior examples of {VR}-based diagnostic platforms are summarized.

Conclusion: Immersive {VR} offers a promising approach for advancing {ADHD} assessment by improving ecological validity, enabling objective multimodal measurement, and supporting more personalized subtype differentiation. Further research may refine its integration into clinical workflows.