Digital twins, often called “virtual replicas” of their underlying systems, enable advanced system analyses during their design, development, and operation. This talk will cover how we utilized digital twins that we built with artificial intelligence techniques in two real-world applications in two Norwegian public sectors for quality assurance of the healthcare services they provide to residents. The first case concerns the Oslo City healthcare department, which, together with several industrial vendors, provides various IoT-based healthcare services to its residents, such as patients’ home care with an industrial IoT platform connecting diverse medical devices, information systems, hospitals, pharmacies, etc. The second case concerns the Cancer Registry of Norway (CRN), which collects and processes cancer-related data and produces relevant data and statistics for various stakeholders (e.g., patients, government, and researchers) with a complex socio-technical software system connecting diverse external systems, e.g., medical laboratories, hospitals, and general practitioners’ software systems. For both cases, we built digital twins with AI-based techniques (e.g., neural networks) to build replicas of these systems to support automated testing at scale with thousands of diverse devices, such as in the case of Oslo City, supporting data validation at CRN and running advanced simulations to test the system. In addition, since these systems undergo continuous evolution, we also built digital twin evolution approaches with advanced AI techniques (e.g., transfer learning and meta-learning) for the cost-effective evolution of digital twins. The talk will present various challenges that we faced when developing such digital twins (e.g., related to personal data). Next, we will present the technical details of the digital twins and their evolution approaches, followed by the key results. Finally, we will present the ongoing work, further research, technical challenges, and issues related to the deployment of digital twins. Due to data privacy concerns and confidentiality agreements, the digital twins and approaches to building digital twins have not been publicly released. However, their reduced implementations are available here:

• https://github.com/Simula-COMPLEX/EvoCLINICAL
• https://github.com/Simula-COMPLEX/MeDeT