Enhancing Clinical Decision Support Systems with Big Data and AI in Medical Informatics

Abstract

By allowing real-time diagnostics, predictive analytics, and automated therapy recommendations, the combination of Artificial Intelligence (AI) and Big Data into Clinical Decision Support Systems (CDSS) has changed healthcare decision-making. While AI-driven models use machine learning (ML), deep learning (DL), and natural language processing (NLP) to improve diagnosis accuracy and clinical efficiency, traditional rule-based CDSS suffered constraints in managing complex and dynamic patient data. With an overall increase of over 30 % in predictive performance, this research assesses the efficacy of AI-powered CDSS against conventional rule-based models by showing notable accuracy, precision, recall, and F1-score improvement. Streaming data processing, edge artificial intelligence, and federated learning further help real-time decision-making to guarantee scalable AI-based interventions. Widespread use depends on the difficulties of data security, model interpretability, and interoperability being overcome. This research highlights the potential, challenges, and future directions of AI-driven CDSS in improving evidence-based, data-driven, and personalized healthcare solutions.