MACHINE LEARNING APPROACHES FOR CLINICAL DECISION-MAKING IN PERSONALIZED MEDICINE

Abstract

Artificial Intelligence (AI) technologies have become pivotal in enhancing clinical decision-making processes within modern healthcare systems. This paper explores the application of AI in supporting clinicians through improved diagnostics, personalized treatment planning, and patient monitoring. We analyze the core principles, architectures, and functionalities of AI-based clinical decision support systems (CDSS), highlighting their advantages in early disease detection and prevention. Additionally, the paper addresses current limitations, ethical concerns, and data security challenges associated with these systems. Through contemporary case studies and practical implementations, the effectiveness and future prospects of AI in clinical environments are discussed. This study aims to provide healthcare professionals, IT specialists, and researchers with comprehensive insights into the integration of AI technologies to improve clinical outcomes and optimize healthcare delivery.