A Review of Challenges, Advancements, and AI-Driven Approaches for Mental Disorder ASD and ADHD Detection

Autism Spectrum Disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are neurodevelopmental conditions that frequently co-occur with mental illnesses such as anxiety, depression, schizophrenia, and bipolar disorder. The complexity of these disorders, coupled with overlapping symptoms, presents significant challenges in accurate diagnosis and effective intervention. Traditional diagnostic methods rely on symptom-based assessments, which are often subjective and prone to misdiagnosis or underdiagnosis. In recent years, artificial intelligence (AI) and deep learning have emerged as promising tools for enhancing diagnostic precision by leveraging neuroimaging (MRI, fMRI), speech, and text-based data. This survey provides a comprehensive review of state-of-the-art AI-driven approaches for diagnosing ASD, ADHD, and associated mental illnesses. It explores the integration of multimodal data sources, discusses current challenges in AI-driven mental health assessments, and highlights ethical and privacy concerns. Furthermore, the paper identifies key research gaps, including the need for large-scale multimodal datasets, improved model interpretability, and real-world clinical validation. By bridging the gap between computational intelligence and clinical practice, this survey aims to pave the way for more accurate, scalable, and personalized mental health diagnostics, ultimately improving early detection and treatment outcomes for individuals with ASD, ADHD, and comorbid conditions.