AI-Driven Digital Twin Ecosystems: Applications in Healthcare, Environmental Engineering, and Smart Enterprise Management

Abstract

Digital twin technology—the creation of dynamic, continuously updated virtual representations of physical systems, processes, or entities—has progressed from a niche manufacturing simulation concept to a foundational paradigm for artificial intelligence (AI)-driven decision support across diverse domains. This paper provides a comprehensive, cross-domain examination of AI-driven digital twin ecosystems, synthesizing their application across three distinct but increasingly convergent fields: healthcare, environmental engineering, and smart enterprise management. We define digital twin ecosystems as integrated, multi-stakeholder technical architectures in which networked digital twins, continuously synchronized with physical-world sensor and operational data, are augmented with machine learning (ML) models to enable predictive simulation, scenario optimization, and autonomous or semi-autonomous decision support. In healthcare, we examine patient-specific physiological digital twins, hospital operations twins, and population health twins, reviewing applications spanning personalized treatment optimization, surgical planning, and epidemic forecasting. In environmental engineering, we examine urban water system twins, watershed-scale hydrological twins, and the integration of digital twin architectures with the predictive stormwater and workforce intelligence systems examined in recent literature. In smart enterprise management, we examine AI-augmented digital twins of organizational processes, supply chains, and—of particular relevance to contemporary enterprise technology practice—human capital systems within Enterprise Resource Planning (ERP) platforms such as SAP, where employee behavioral and well-being digital twins are emerging as a frontier application. We propose a unifying Cross-Domain Digital Twin Maturity Framework characterizing digital twin sophistication across five levels, from static descriptive models to autonomous prescriptive ecosystems, and examine the technical infrastructure—including Internet of Things (IoT) sensing, edge and cloud computing, and generative AI integration—that underpins contemporary digital twin implementations. Ethical and governance considerations, including data privacy, model fidelity risk, and the equity implications of differential digital twin access, are critically examined. The paper concludes that digital twin ecosystems, despite their domain-specific technical particularities, share sufficient architectural and governance commonality to warrant a unified cross-disciplinary research and practice agenda, and proposes priority directions for advancing interoperable, ethically governed digital twin infrastructure across the domains examined.