Opportunities, Challenges and Future in ADHD Detection using AI-Powered Models: A literature Review

Attention Deficit Hyperactive Disorder (ADHD) is a neurodevelopmental condition that can impact individuals of all ages, marked by symptoms such as difficulty focusing excessive activity, and a tendency to act without thinking. Associated with low dopamine levels and reduced metabolic activity in brain regions governing attention and motor control, ADHD diagnosis remains challenging. This review examines deep learning (DL) approaches and diagnostic tools for ADHD detection, emphasizing critical gaps and suggest a new method for improving the accuracy of ADHD detection. Current studies predominantly rely on EEG signals and resting-state fMRI, with limited use of audio data. Promising modalities like MEG and actigraphy remain underexplored, and the scarcity of multimodal datasets weakens model generalizability. Most DL models employ basic architectures like CNNs or ANNs, neglecting advanced, interpretable frameworks that could boost accuracy. Future efforts may emphasize the use of multimodal information and interpretable AI approaches to enhance the precision and trustworthiness of ADHD detection. Moreover, incorporating digital biomarkers and longitudinal datasets can support the development of personalized and clinically relevant diagnostic solutions.