The rapid convergence of generative artificial intelligence, cyber-physical systems, and digital twin technologies has created a transformative inflection point for both advanced manufacturing and data-intensive healthcare. Across these sectors, organizations are increasingly compelled to integrate heterogeneous sensor streams, automate decision-making, and ensure cyber-physical trustworthiness under strict regulatory and safety constraints. While the literature has developed rich but fragmented insights into artificial intelligence in manufacturing, generative modeling in healthcare, and standardization for cyber-physical interoperability, a unified conceptual and methodological framework for generative AI–driven sensor fusion within secure digital twin ecosystems remains underdeveloped. This gap is especially problematic given the escalating reliance on digital twins for real-time operational control, predictive maintenance, personalized clinical pathways, and safety-critical diagnostics.

This study develops and elaborates an integrated research framework that positions generative AI–based sensor fusion as the epistemic core of secure digital twin ecosystems. The framework draws conceptually from manufacturing 4.0 scholarship, healthcare informatics, and standardization-aligned cyber-physical systems research, synthesizing these literatures into a coherent architecture that addresses synchronization, probabilistic reasoning, fault detection, and reliability at scale. Central to this synthesis is the generative AI sensor fusion paradigm articulated by Hussain et al. (2026), which provides a standardization-aligned theoretical anchor for secure digital twins across cyber-physical domains. By situating this paradigm within broader debates on AI augmentation versus automation, ethical and governance challenges, and sector-specific performance imperatives, the article demonstrates how generative models move beyond predictive analytics to become epistemic engines of cyber-physical understanding.

Methodologically, the study employs a qualitative, theory-driven synthesis of manufacturing, healthcare, and AI standardization literature to construct a multi-layered analytical model. This model explicates how heterogeneous data sources, ranging from industrial sensors to electronic health records and medical imaging systems, can be fused through generative architectures into probabilistically coherent digital twins. The approach also critically examines interoperability standards, cybersecurity constraints, and organizational governance structures that shape the real-world viability of such systems.

The results of this synthesis indicate that generative AI sensor fusion enables digital twins to transition from static simulation artifacts into adaptive, self-updating epistemic infrastructures. In manufacturing, this manifests as predictive maintenance, autonomous quality control, and energy-aware optimization, while in healthcare it supports real-time patient modeling, care pathway optimization, and personalized treatment planning. Across both domains, the reliability and trustworthiness of these outcomes depend on standardization-aligned synchronization and fault detection mechanisms that mitigate data drift, adversarial manipulation, and system fragility, as emphasized by Hussain et al. (2026).

The discussion extends these findings into a broader theoretical and policy discourse, addressing the implications of generative digital twins for labor, professional autonomy, ethics, and global technological governance. By comparing divergent scholarly positions on AI augmentation, automation, and socio-technical risk, the article argues that secure digital twin ecosystems require not only technical excellence but also institutional alignment with international standards, ethical toolkits, and interoperable data infrastructures.

In conclusion, this research establishes generative AI–driven sensor fusion as a foundational paradigm for secure digital twin ecosystems that span manufacturing and healthcare. It offers a theoretically grounded and practically oriented framework that advances both scholarly understanding and policy-relevant design principles for the next generation of cyber-physical systems.

Generative artificial intelligence, Digital twins, Sensor fusion

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