GS4 Student Scholars Symposium

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD.

The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified through eye-tracking technology and analyzed using software algorithms.

These technologies have shown promise in improving diagnostic accuracy, with machine learning models further enhancing their potential. However, the effectiveness of these interventions varies across age groups and study designs, highlighting the need for further research to refine these tools for clinical application.

Eye-tracking technology and assessment software provide a valuable supplement to traditional diagnostic methods but require further validation and standardization before widespread clinical use in children and adults.