Cross-Domain Digital Twin Integration

State of the evidence

The evidence establishes that Digital Twins (DTs) have transitioned from manufacturing to civil infrastructure 10, clean energy 5, and healthcare 7,9. While the technical capacity for real-time monitoring via IoT and BIM is well-documented 3,8,10, there is a persistent gap in integrating intelligent decision-support models into these frameworks to ensure reliable asset maintenance 1. Furthermore, the underlying network infrastructure for these systems often suffers from security and performance weaknesses in 'best-effort' IoT architectures 6.

Key findings

• Digital Twins in bridge management are hindered by conceptual ambiguities and a lack of integrated decision-support models for maintenance reliability 1. 1
• Bridge Information Modeling (BrIM) allows for the replacement of paper-based documentation with digital twins integrating geometric and inspection data 3. 3
• Deterministic silicon photonics switches can address security and performance gaps in Industrial-Tactile IoT by creating interference-free virtual networks 6. 6
• In oncology, dynamic DTs integrate multimodal patient data (clinical, imaging, molecular) to simulate therapy scenarios in real-time 7. 7
• High-resolution smart meter data enables real-time hydraulic simulation and dynamic demand assignment in water distribution systems 8. 8
• AI-powered DTs in healthcare enable personalized medicine by mirroring individual patient profiles to predict outcomes across heart, diabetic, and respiratory care 9. 9

Where the evidence disagrees

• While DTs are praised for improving reliability 5,10, P0 explicitly notes that the lack of decision-support models currently hinders the practical reliability of maintenance decisions.

Open opportunities

What the research supports that no idea in the catalog takes yet.

• Automated Bridge Maintenance Decision-Support Engine

  P0 identifies a specific lack of decision-support models in bridge DTs; a tool that converts DT data into actionable maintenance schedules would fill this gap.

• Real-time Water Demand Forecasting Interface for Utilities

  P7 demonstrates that high-resolution smart meter data exists but is underutilized for real-time hydraulic simulation in operational settings.

• Multimodal Oncology Simulation Workbench

  P6 suggests a shift from 'trial and error' to simulation in oncology using multimodal data, which is distinct from the chronic disease focus of I11.

• Deterministic Network Slicing Orchestrator for Industrial IoT

  P5 proposes deterministic silicon photonics to create secure virtual networks; a software orchestrator to manage these slices would be a buildable product.

References

1. 1.Evolution of Digital Twin Frameworks in Bridge Management: Review and Future DirectionsVahid Mousavi, Maria Rashidi, Masoud Mohammadi et al. · 2024 · 58 citations
2. 2.[SUPERSEDED] Physics-Informed AI and Quantum Technologies for Certifiable Real-Time Control of Compact Fusion Generators — reproducibility deposit (code, data, figures)Priyanca Ford · 2026 · 360 citations
3. 3.Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructuresMasoud Mohammadi, Maria Rashidi, Yang Yu et al. · 2023 · 95 citations
4. 4.Challenges in the application of digital transformation to inspection and maintenance of bridgesMarcos Massao Futai, Túlio Nogueira Bittencourt, Hermes Carvalho et al. · 2022 · 52 citations
5. 5.Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key ChallengesM Kim, Fatemeh Ghobadi, Amir Saman Tayerani Charmchi et al. · 2025 · 23 citations
6. 6.Securing the Industrial-Tactile Internet of Things With Deterministic Silicon Photonics SwitchesTed H. Szymanski · 2016 · 52 citations
7. 7.From data-driven cities to data-driven tumors: dynamic digital twins for adaptive oncologyİrem Karaman, Burhan Sebin · 2025 · 19 citations
8. 8.Streaming Smart Meter Data Integration to Enable Dynamic Demand Assignment for Real-Time Hydraulic SimulationM. Ehsan Shafiee, Amin Rasekh, Lina Sela et al. · 2020 · 27 citations
9. 9.Digital twins in healthcare: a review of AI-powered practical applications across health domainsZiad Elgammal, M. Taleb Albrijawi, Reda Alhajj · 2025 · 9 citations
10. 10.Advancing civil infrastructure with digital twins: a review of applications and challengesHessam Kaveh, Reda Alhajj · 2025 · 5 citations