REAL-TIME CLINICAL DECISION SUPPORT SYSTEMS: LEVERAGING AI AND BIG DATA FOR DISEASE DIAGNOSIS, PROGNOSIS, AND TREATMENT OPTIMIZATION

Abstract

In this contemporary age, Real-Time Clinical Decision Support Systems (CDSS) have become indispensable in healthcare settings where artificial intelligence (AI) and big data analytics converge to form a platform for improved disease diagnosis, prognosis, and treatment optimization. Machine learning (ML), deep learning (DL), and natural language processing (NLP) use these systems to sift through massive datasets of EHRs, laboratory results, genomic data, and medical imaging. CDSS has facilitated its operational purpose by providing real-time insights from data to speed up clinical procedures, increase diagnostic precision, and personalize treatment options. The increased complexity of medical data brings issues related to interoperability of data, explainability of the model, and ethical issues with AI-driven decision-making. Algorithmic bias and data privacy are issues that can only be tackled after sound regulatory frameworks have been established alongside fair AI models. This paper provides a complete review of real-time CDSS with analyzable methodologies in AI, their applications, and their effects on clinical decision-making. An extensive review of the literature and empirical findings is used to assess the growths, present challenges, and possible future directions of AI-powered CDSS. To this end, we propose possible strategies like explainable AI (XAI), federated learning, and integration of wearables in health with the view of enhancing precision medicine and improving patient outcome..