Artificial Intelligence-Enhanced Digital Twins: A Review of Algorithms, Applications, and Implementation Barriers
Keywords:
Algorithms, Artificial Intelligence, Big Data, Cyber-Physical Systems, Digital Twin, Industry 4.0, Internet of Things, Machine Learning, Predictive Analytics, Real-Time Monitoring, Reinforcement Learning, Simulation Models.Abstract
Artificial Intelligence-Enhanced Digital Twins (AI-DTs) have been a revolutionary paradigm that allows real-time monitoring, prediction, and optimization of complex physical systems. By combining advanced AI algorithms and dynamic virtual replicas, AI-DTs play a major role in improving decision-making accuracy, system adaptability, and operational efficiency in a variety of sectors, including manufacturing, healthcare, smart cities, transportation, and energy. This review discusses the key AI methods such as machine learning, deep learning, reinforcement learning and predictive analytics that enable Digital Twins to learn from data, predict the behavior of systems and respond specifically to changing conditions. Additionally, the paper identifies key application areas where AI-DTs have had significant impact and measurable impact, including predictive maintenance, process automation, personalized medicine, structural health monitoring, and intelligent infrastructure management. Despite their emerging adoption, widespread implementation of AI-DTs faces a number of critical barriers, such as data integration challenges, computational complexity, cybersecurity risks, model interpretability issues and the lack of standardized frameworks. This review synthesizes existing research to lay out a comprehensive understanding of the technological landscape, identifies key gaps that limit real-world deployment, and outlines future research directions that aim to enable more secure, scalable, and interoperable AI-driven Digital Twin eco-systems
