Digital Twins and Intelligent Manufacturing for Breast Cancer Devices

Abstract

This chapter surveys how digital twins (DTs) and intelligent manufacturing are reshaping the lifecycle of breast-cancer medical devices through Internet of Things (IoT)-driven data flows. We review architectures that link design, production, clinical use, and post-market surveillance via a “digital thread,” enabling continuous feedback between factory and clinic. On the manufacturing floor, we analyze model-based design, in-line sensing, and edge analytics for process control, traceability, and rapid changeover, covering modalities such as diagnostic wearables, imaging adjuncts, biopsy tools, and implantable systems. For clinical deployment, we examine patient- and fleet-level DTs that integrate device telemetry with electronic health record (EHR)-adjacent data to support calibration, predictive maintenance, and personalized therapy support. The chapter proposes a reference framework spanning edge–fog–cloud layers, data models, and control loops, and synthesizes evidence on benefits (reduced defect rates, shorter release cycles, and improved reliability) alongside open challenges in interoperability, verification/validation of twin models, cybersecurity, privacy, and regulatory alignment. We also outline key performance indicators (KPIs) for assessing maturity, process capability, time-to-release, real-world performance drift, and post-market vigilance responsiveness. Future directions include physics-informed and hybrid (data + mechanistic) twins, standardized digital threads across suppliers, federated analytics for privacy-preserving learning, and zero-trust security patterns. Taken together, the chapter offers a practical roadmap for translating IoT-enabled DTs from promising pilots to scalable, compliant manufacturing and clinical value in breast-cancer device ecosystems.