The Future of Paediatric Clinical Data Management AI-Driven Databases

The integration of artificial intelligence (AI) into clinical databases offers transformative potential for improving paediatric healthcare data management. This paper presents the design and development of a clinical database system augmented with an AI-powered interface, specifically tailored for the storage and retrieval of paediatric data. Leveraging advanced AI technologies such as GPT-based models for natural language processing (NLP) and BERT for contextual understanding, the interface supports seamless data entry, intelligent querying, and predictive analytics to enhance clinical decision-making. The system addresses challenges in managing high volumes of heterogeneous paediatric clinical data, including patient histories, diagnostic records, and treatment outcomes. Paediatric-specific considerations, such as the integration of growth metrics and age-dependent health parameters, are central to the design. Ensuring compliance with data privacy regulations and ethical guidelines, the platform emphasizes secure and responsible handling of sensitive patient information. Initial evaluations highlight the system's ability to streamline workflows, improve data accuracy, and reduce administrative burden for healthcare professionals. By enabling more efficient interaction with clinical data, this AI-enhanced database has the potential to elevate paediatric care quality and support advanced research in child health. Future development will focus on expanding interoperability with existing electronic health record (EHR) systems, incorporating machine learning models for personalized care recommendations, and validating performance in diverse healthcare settings.