A roadmap for AI-aided digital twins in computational nuclear oncology: A position paper by the EANM

Key objectives of precision medicine include improving treatment efficacy, reducing side effects, preventing disease, optimizing healthcare resources and costs, and ensuring that treatments are cost-effective while ultimately enhancing overall health outcomes. It matches therapies to patients most likely to benefit, accounts for individual differences in drug metabolism and responses, identifies individuals at higher risk, and implements preventive measures. Digital twins, creating virtual computational replicas of physical systems, processes, or entities, offer a unique opportunity to support personalized medicine through real-time simulation, monitoring, and optimization. In this roadmap, we propose recommendations on the workflow and methodology, describing how AI can be applied within image-based digital twin systems in nuclear computational oncology. We unite interdisciplinary expertise to efficiently guide the research and drive computational nuclear oncology toward clinical applications. We provide definitions, key insights, and a clear framework for each step in the development of a virtual system. These include: 1) Data Extraction and Digital Twin Creation, 2) Tracer Diffusion Simulation, 3) Scaling to Macroscale Tumor Analysis, and 4) Dynamic Updates and Continuous Learning. We discuss both clinical and technical perspectives, challenges, and promising future directions to bridge the gap between “real systems” and “virtual replicas”.