TRANSFORMING HEALTHCARE DECISION-MAKING: THE ROLE OFAI IN EVIDENCE-BASED MEDICINE

Abstract

Artificial intelligence (AI) integrated in health care portends to be a needle of transformative potential in providing evidence-based medicine as advancements in diagnostic precision and personalized treatment and patient care optimization. This paper explores the cutting edge of technologies including machine learning, natural language processing and predictive analytics in the pivotal role of AI in EBM. Through AI analysis of vast datasets, the methodology supports the use of clinical decision making that reduces human error and enables the development of optimal, personalized treatment plans for each patient. And we survey current AI uses in healthcare, such as AI based diagnostic tools, AI based predictive disease progression models, and AI based clinical decision support systems (CDSS). We also discuss the challenges and the ethical aspects of AI integration, especially issues related to data privacy, algorithmic bias and require for effective regulatory measures. We show how AI can strengthen EBM in practice through case studies and real world examples. Finally, the paper has a look forward to future prospects and novel trends in AI showing p.s.otential to transform healthcare delivery beyond even what is imagined today. Included are advances in the AI algorithms used in health care, the increased inter operability of AI systems in health care infrastructures, and the opportunity for AI to enable broader and more comprehensive, and thus inclusive, medical research. Our results highlight the importance of systems where technological innovation is carefully balanced with ethical considerations in realizing the full potential in AI for transforming healthcare clinical decision making and patient outcomes.