BALANCING DATA PRIVACY AND OPEN ECOSYSTEMS IN ADHD AI-DIAGNOSTICS: A BLOCKCHAIN-BASED APPROACH

Attention-Deficit/Hyperactivity Disorder (ADHD) affects between 5% and 7% of school-aged children globally, generating cumulative economic losses estimated at hundreds of billions of dollars annually. The deployment of artificial intelligence systems utilizing Graph Convolutional Networks to analyze electroencephalographic signals unlocks opportunities for objective, highly accurate diagnostics of the disorder, achieving a classification accuracy of up to 97.29%. However, the efficacy of such systems fundamentally depends on access to large-scale cross-institutional datasets, which stands in direct contradiction to statutory requirements protecting sensitive personal information, particularly the data of minors. This paper investigates how decentralized blockchain technologies can reconcile this contradiction by establishing compliant, patient-centric open data-sharing ecosystems for ADHD diagnostics. Based on a platform architecture piloted across three neurological clinics, it is demonstrated that data standardization via the FHIR protocol, dynamic consent managed through smart contracts, and cryptographic depersonalization concurrently satisfy the regulatory mandates of the GDPR (EU), LGPD (Brazil), and HIPAA (US), while delivering a 19% improvement in the quality of early diagnostics. The economic impact resulting from the reduction in diagnostic delay is estimated at USD 2.7 billion to 3.6 billion annually for Brazil alone. The paper concludes with specific regulatory recommendations for legislators and data protection authorities.