Quantum Network-Driven AI Models for Predictive Healthcare
Chetan Jagannath Shelke, Kahtan A. Mohammed, Valureddi Revathi, T. Pravalika, Alok K. Jain, Joshuva Arockia Dhanraj · Advances in computational intelligence and robotics book series · 2024
The incorporation of AI models that are driven by networks into the healthcare industry has the potential to rethink the concept of predictive medicine. The purpose of this investigation is to investigate the creation and operation of hybrid models that combine artificial intelligence and amount computing to improve the accuracy and precision of healthcare forecasts. These artificial intelligence models can reuse complex medical data more effectively than traditional methods because they make use of the enormous community and processing capacity of internet networks. The purpose of this project is to develop and perfect early complaint findings, treatment strategies that are tailored to individual cases, and predictive analytics for a variety of health disorders. The initial findings point to considerable gains in both the accuracy of the prediction and the speed at which it is processed, demonstrating the promise of artificial intelligence in the field of healthcare. The purpose of this paper is to provide a comprehensive analysis of the methodologies.