[Formula: see text] Artificial intelligence as a support to diagnose ADHD: an insight of unorthodox approaches: a scoping review.

Artificial intelligence (AI) is reshaping the landscape of attention deficit hyperactivity disorder (ADHD) diagnosis through data-driven and technology-enhanced methodologies. This scoping review, conducted in accordance with PRISMA guidelines, systematically analyzed 54 studies published over the past two decades to assess AI's role in ADHD detection and evaluation. The included studies primarily explored AI applications in brain imaging (MRI), brain activity monitoring (EEG and ECG), behavioral assessments, virtual reality-based testing, and motion-tracking sensors. Among the AI technologies examined, machine learning (ML) and deep learning (DL) algorithms demonstrated promising diagnostic accuracy, with performance rates ranging from 70% to 95%. Convolutional neural networks (CNNs) and support vector machines (SVMs) were particularly effective in image and signal analysis, while natural language processing (NLP) models showed potential in behavioral and cognitive assessments. Despite these advancements, challenges such as algorithmic bias, inconsistent data quality, and the need for extensive, diverse datasets remain barriers to widespread clinical integration. Moreover, while AI models enhance speed and precision in ADHD detection, their applicability in treatment monitoring and personalized intervention remains an area for future research. This review underscores the transformative potential of AI in ADHD diagnosis and advocates for a hybrid approach that integrates AI-driven tools with traditional clinical assessments to enhance diagnostic reliability and patient outcomes.