ADHD Diagnostic Tools Across Ages: Traditional and Digital Approaches

Marina Knyazhansky^*Tammar Shrot

• Sami Shamoon College of Engineering, Ashdod, Israel

This article presents a narrative review of current approaches to the diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) in children and adults. We place particular attention on recent technological advancements. ADHD diagnosis traditionally relies on a combination of subjective rating scales, clinician interviews, and observational data. In recent years, objective tools have emerged, including computerized neuropsychological tests and biometric measures. Examples include electroencephalography and eye tracking. Their clinical utility remains under investigation. This review explores these developments, including the integration of virtual reality environments and machine learning algorithms into diagnostic processes. We synthesize findings from diverse sources. The review highlights both established and emerging tools and the age-group differences in diagnostic challenges. We also note the potential of immersive and data-driven technologies to improve accuracy. Rather than applying a systematic methodology, this narrative review aims to capture current directions and preliminary insights that can inform future research and practice. We reviewed recent research on ADHD diagnosis across age groups, with a focus on virtual reality and machine learning. We found that these tools showed modest accuracy improvements and better reflection of real-world setting, though studies were generally small and diverse. These findings suggest that VR-ML systems could develop into practical and explainable decision-support tools for everyday ADHD diagnosis.