AI-Driven Predictive Modeling In Healthcare: A Data Science Perspective On U.S. Healthcare Data

Abstract

AI-based predictive modeling has emerged as a promising concept in the healthcare domain, with this paper reviewing its utility in the United States healthcare system specifically. Due to the reliance on big healthcare data such as patient records, medical imaging, and genomic data, predictive models driven by AI and machine learning are used to predict disease outbreaks, forecast the result of a patient, and optimize decision-making processes. AI can revolutionize predictive healthcare modeling by enabling early diagnosis, personalized treatment plans, and optimal resource allocation in hospitals. The paper highlights how several methods, e.g., supervised learning, deep learning, and reinforcement learning can be deployed to predict the risk of diseases in patients, formulate treatment timetables, and enhance the efficiency of the patient care delivery process. The paper highlights the ethical challenges, privacy issues, and regulatory implications associated with implementing AI in healthcare, especially regarding the Health Insurance Portability and Accountability Act (HIPAA) compliance. The report sheds light on the significant role that AI plays in healthcare, and the need for ongoing innovation that is based on data, in order to create a smarter and more accessible healthcare system for Americans.