Paper Contents
Abstract
Artificial Intelligence (AI) has been introduced as a tool in healthcare, and it has created the possibility of enhancing patient outcomes using predictive analytics. This study examines how AI-based models are transforming the idea of pro-actively predicting and diagnosing as well as managing diseases earlier on before they worsen. The main objective of the research is to examine how well the AI methods, specifically machine learning and deep learning can predict medical conditions and improve clinical decision-making.An analysis of existing literature and case studies was done to test different AI models including decision trees, neural networks, and support vector machines. These models were studied in terms of their use in such aspects as early disease detection, prediction of readmission, and individualized treatment planning. The research also focuses on the application of natural language processing (NLP) in deriving insights out of unstructured clinical data.The most important results imply that AI can greatly enhance predictive accuracy, in particular, the identification of high-risk patients and the allocation of resources.The study finds that although AI presents an unprecedented potential in predictive healthcare analytics, its ethical use and its integration into the current systems should be considered carefully. As AI continues to develop and be regulated, it will become an indisputable asset of the future of preventive and individualized medicine.
Copyright
Copyright © 2025 Tavishi Gupta. This is an open access article distributed under the Creative Commons Attribution License.
