<![CDATA[This research delves into the development and implications of an expert system leveraging artificial neural networks (ANNs) for diagnosing neurological disorders in pediatric patients. By amalgamating the intricacies of pediatric neurology with cutting-edge AI technology, this study endeavors to enhance diagnostic accuracy, facilitate timely interventions, and personalize treatment strategies for children grappling with neurological conditions. The research elucidates the architectural framework, data processing methodologies, and training techniques employed in constructing the expert system. The integration of diverse neurological data sources, including EEG readings, MRI scans, and patient histories, underpins the system's capability to discern patterns and generate accurate diagnoses. Ethical considerations loom prominently throughout this exploration, emphasizing the paramount importance of patient data privacy, informed consent, transparency in AI decision-making, and the collaborative role of AI alongside healthcare providers. The study culminates in unveiling the practical implications of implementing such an expert system in real clinical settings. Usability, reliability, regulatory compliance, and the intricate dynamics of human-AI interaction in healthcare emerge as critical pillars in the successful integration of AI-driven diagnostic tools.]]>
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