Author(s) :

Mahantesh Shikari.

Volume/Issue :

Abstract :

The rapid expansion of electronic health records (EHRs) and medical narratives has transformed healthcare into a data-rich domain. Yet, extracting meaningful insights from these vast repositories remains a complex challenge. Artificial Intelligence (AI)-driven clinical data mining emerges as a powerful tool to uncover hidden patterns, enhance diagnostic precision, and optimize patient outcomes. This paper presents an in-depth exploration of AI techniques used in clinical data mining, focusing on structured EHR data and unstructured medical narratives. It discusses foundational concepts, key methodologies, real-world applications, challenges, and future directions. Case studies illustrate how AI systems have improved early disease detection, personalized treatments, pharmacovigilance, and healthcare resource management. The paper concludes by emphasizing the need for ethical frameworks, interpretability, and continuous innovation to fully harness the transformative potential of AI in clinical data mining.

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