Computational Oncology and the Augmented Oncologist: How Implementation-Ready Ai and Digital Twins Will Transform Education, Research, and Practice in Precision Oncology—Insights From Theranostics Publisher

Summary: Could digital twins reshape cancer care? A perspective proposes AI and digital twins in precision oncology; clinical gains remain unproven. #PrecisionOncology #DigitalHealth

Abdollahi H ; Fayaz Bakhsh A ; Klyuzhin I ; Soltani M ; Saboury B ; Rahmim A
Source: Frontiers in Biomedical Technologies Published:2026

Abstract

Theranostics is emerging as a powerful modality in precision oncology, integrating diagnostic imaging with targeted therapies to enable more effective and individualized cancer management. In parallel, Artificial Intelligence (AI) and Digital Twin (DT) technologies are increasingly being explored as enabling frameworks for advancing research, education, and clinical decision support. AI facilitates a range of quantitative and workflow-driven tasks, including organ and lesion segmentation, longitudinal lesion matching and tracking, and absorbed dose estimation, while also contributing to evidence generation, implementation, and evaluation processes. Complementing this, DTs integrate multimodal images, pharmacokinetic models, molecular characteristics, and clinical data to create dynamic, patient-specific representations of disease and treatment response. Together, these technologies improve treatment response and outcome prediction, enhance treatment planning, and support more adaptive and data-informed disease management strategies. In the near term, clinical practitioners and trainees must learn to effectively supervise AI systems, understand algorithmic limitations, and ensure their safe and effective use within clinical workflows. Over time, DT-enabled environments may support immersive, simulation-based learning with continuous feedback and exposure to complex or rare clinical scenarios, reshaping professional training. More broadly, the convergence of AI and DT technologies is driving an evolution in the structure of oncology practice itself. Alongside the four established clinical specialties medical oncology, radiation oncology, surgical oncology, and nuclear oncology a complementary role is emerging: computational oncology. These clinical and computational roles operate synergistically within an integrated health system, advancing data-driven and patient-centered precision oncology. Copyright © 2026 Tehran University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by-nc/4.0/). Noncommercial uses of the work are permitted, provided the original work is properly cited.