ARTIFICIAL INTELLIGENCE–DRIVEN APPROACHES TO CLINICAL DECISION SUPPORT SYSTEMS

The integration of Artificial Intelligence (AI) into Clinical Decision Support Systems (CDSS) has significantly reshaped contemporary healthcare practices by enhancing diagnostic precision, predictive capabilities, and therapeutic decision-making. This research investigates the role and impact of AI-driven tools in assisting clinicians throughout various stages of the medical decision-making process. The study systematically examines the advantages, limitations, and challenges associated with the adoption of AI technologies within healthcare systems. The methodological approach involves an extensive review of existing literature, a comparative analysis of prominent AI-based decision support tools, and an evaluation of selected clinical case studies. The findings demonstrate that the incorporation of AI in CDSS contributes to improved diagnostic accuracy, more reliable predictive analytics, and the development of optimized and personalized treatment strategies. Furthermore, the study discusses the ethical, technical, and operational challenges that accompany the integration of AI into clinical workflows, emphasizing the need for transparency, data security, and clinician training. In conclusion, the paper presents a set of practical recommendations for the effective implementation of AI in clinical practice and outlines potential directions for future research aimed at advancing intelligent, reliable, and ethically sound healthcare decision-support systems.