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

Srikant Kumar Dhar, Kotte Navyav, Kanika Seth, Dikshit Sharma, Sourabh Kumar Singh and Shashikant Patil

Seminars in Medical Writing and Education, 2024, vol. 3, 503

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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:medicw:v:3:y:2024:i::p:503:id:503

DOI: 10.56294/mw2024503

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