AI in Healthcare: Predictive Analytics and Disease Diagnosis Systems

Abstract

The integration of artificial intelligence (AI) into healthcare signifies a transformative shift in medical service delivery, diagnosis, and patient outcome enhancement. This comprehensive literature review examines the profound impact of AI-driven predictive analytics on healthcare, particularly in disease progression, treatment response, and recovery rates. By analyzing vast datasets, including electronic health records, imaging, and genetic data, AI technologies have optimized treatment plans and enhanced recovery through advanced predictive capabilities. The review highlights the evolution of AI from simple automation to sophisticated machine learning and deep learning applications, emphasizing its role in Clinical Decision Support Systems (CDSS) and the optimization of healthcare delivery. Moreover, it addresses the ethical considerations essential for responsible AI implementation, such as data privacy and algorithmic bias. The findings underscore the potential of AI in revolutionizing clinical decision-making and healthcare delivery, while also calling for ongoing research and validation to ensure effective and ethical use. Ultimately, this review provides a comprehensive understanding of AI methodologies, applications, challenges, and future directions, offering insights into its transformative role in enhancing patient care and optimizing healthcare systems. Keywords: Artificial Intelligence, CDSS, Disease diagnosis, Machine Learning, Health Management