Artificial intelligence-based digital twin framework for circular economy optimization in healthcare waste management

Health care systems have grown to produce ever growing amounts of garbage, which further strain the environment, costs of running the system, and regulatory demands especially in the post pandemic period. The traditional methods of managing linear waste are also inefficient and do not well comply with a circular economy. The paper introduces an artificial intelligence-based, scientifically supported digital twin system that aims at maximizing the effectiveness of healthcare waste management through the application of real time sensing and analytics based on predictive algorithms and through the use of multi objective optimization that is integrated into the circle economy paradigm. The framework virtualizes the streams of waste materials in the hospital through Internet of Things based monitoring and implements the machine learning models to forecast, assess, and analyze waste and provide an optimization engine that balances the goals of both economy and environmental objectives within the regulatory constraints. Statistical results showed that there were significant and significant increases in all the key indicators. The average recycling rate rose by 20.0 to 50.7 percent which is the 30.7 percentage point, and the sample t testing group has verified that the difference is significant at p < 0.001, with a statistical power of 0.99. Statistically significant findings prove that the value of artificial intelligence facilitated digital twins can be used to provide the measurable, reproducible, and scalable benefits in terms of circularity, cost-effectiveness, and regulatory compliance in the management of healthcare waste and are justified in their integration into a data driven model of a sustainable approach to operating the hospital.