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
-
Hamid Abdollahi
,
Ahmad Fayaz-Bakhsh
,
Ivan Klyuzhin
,
Madjid Soltani
,
Babak Saboury
,
Arman Rahmim
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.
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33- Luca Marelli and Giuseppe Testa, "Scrutinizing the EU general data protection regulation." Science, Vol. 360 (No. 6388), pp. 496-98, 2018/5/4 (2018).
34- Kim Murphy-Abdouch, "Patient access to personal health information: Regulation vs. Reality." Perspect. Health Inf. Manag., Vol. 12p. 1c, 2015/1/1 (2015).
35- Xuan Gong et al., "Federated learning with privacy-preserving ensemble attention distillation." IEEE Trans. Med. Imaging, Vol. 42 (No. 7), pp. 2057-67, 2023/7 (2023).
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2- Seyed Ali Mirshahvalad et al., "Theranostic: A primer for radiologists." Can. Assoc. Radiol. J., Vol. 77 (No. 1), pp. 202-11, 2026/2 (2026).
3- Lisa Bodei, Ken Herrmann, Heiko Schöder, Andrew M. Scott, and Jason S. Lewis, "Radiotheranostics in oncology: current challenges and emerging opportunities." Nat. Rev. Clin. Oncol., Vol. 19 (No. 8), pp. 534-50, 2022/8 (2022).
4- Eric J. Topol, "High-performance medicine: the convergence of human and artificial intelligence." Nat. Med., Vol. 25 (No. 1), pp. 44-56, 2019/1 (2019).
5- Andre Esteva et al., "A guide to deep learning in healthcare." Nat. Med., Vol. 25 (No. 1), pp. 24-29, 2019/1 (2019).
6- Fathia Ghribi and Fayçal Hamdaoui, "Innovative deep learning architectures for medical image diagnosis: a comprehensive review of convolutional, recurrent, and transformer models." Vis. Comput., Vol. 41 (No. 13), pp. 11603-28, 2025/10 (2025).
7- Hamid Abdollahi et al., "Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies." Theranostics, Vol. 14 (No. 9), pp. 3404-22, 2024/5/27 (2024).
8- H. Abdollahi and A. Rahmim, "Digital Twins for Personalized Healthcare: Application to Radiopharmaceutical Therapies." Frontiers in Biomedical Technologies, Vol. 1 (No. 1), pp. 1-5, 2022 (2022).
9- Arman Rahmim et al., "Theranostic digital twins for personalized radiopharmaceutical therapies: Reimagining theranostics via computational nuclear oncology." Front. Oncol., Vol. 12p. 1062592, 2022/12/15 (2022).
10- Julia Brosch-Lenz, Carlos Uribe, Arman Rahmim, and Babak Saboury, "Theranostic Digital Twins: An indispensable prerequisite for personalized cancer care." J. Nucl. Med., 2022/12/1 (2022).
11- Hamid Abdollahi, Ali Fele-Paranj, and Arman Rahmim, "Model-informed radiopharmaceutical therapy optimization: A study on the impact of PBPK model parameters on physical, biological, and statistical measures in 177Lu-PSMA therapy." Cancers (Basel), Vol. 16 (No. 18), p. 3120, 2024/9/10 (2024).
12- Ali Fele-Paranj, Babak Saboury, Carlos Uribe, and Arman Rahmim, "Physiologically based RadioPharmacoKinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: Studies of non-linearities, multi-bolus injections, and albumin binding." EJNMMI Radiopharmacy and Chemistry, In Press, 2024, NaN/NaN/6.
13- Anahita Piranfar, M. Soltani, Farshad M. Kashkooli, Carlos F. Uribe, and Arman Rahmim, "Spatiotemporal modeling of radiopharmaceutical transport in solid tumors: Application to 177Lu-PSMA therapy of prostate cancer." Comput. Methods Programs Biomed., Vol. 245p. 108004, 2024/3/1 (2024).
14- Lidia Strigari et al., "Computational nuclear oncology toward precision radiopharmaceutical therapies: Ethical, regulatory, and socioeconomic dimensions of theranostic digital twins." J. Nucl. Med., Vol. 66 (No. 5), pp. 748-56, 2025/5/1 (2025).
15- Yosri A. Fahim, Ibrahim W. Hasani, Samer Kabba, and Waleed Mahmoud Ragab, "Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives." Eur. J. Med. Res., Vol. 30 (No. 1), p. 848, 2025/9/23 (2025).
16- Nalan Karunanayake, "Next-generation agentic AI for transforming healthcare." Informatics and Health, Vol. 2 (No. 2), pp. 73-83, 2025/9 (2025).
17- Gokce Belge Bilgin et al., "Theranostics and artificial intelligence: new frontiers in personalized medicine." Theranostics, Vol. 14 (No. 6), pp. 2367-78, 2024/3/25 (2024).
18- Julia Adler-Milstein et al., "Meeting the moment: Addressing barriers and facilitating clinical adoption of artificial intelligence in medical diagnosis." NAM Perspect., Vol. 20222022/9/29 (2022).
19- Amin I. Kassis and S. James Adelstein, "Radiobiologic principles in radionuclide therapy." J. Nucl. Med., Vol. 46 Suppl 1 (No. 1 suppl), pp. 4S-12S, 2005/1 (2005).
20- Ehsan Abadi et al., "Virtual clinical trials in medical imaging: a review." J Med Imaging (Bellingham), Vol. 7 (No. 4), p. 042805, 2020/7 (2020).
21- Fiona J. Leggat, Ross Wadey, Melissa C. Day, Stacy Winter, and Phoebe Sanders, "Bridging the know-do gap using integrated knowledge translation and qualitative inquiry: A narrative review." Qual. Res. Sport Exerc. Health, Vol. 15 (No. 2), pp. 188-201, 2023/3/4 (2023).
22- Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi, Abhinav K. Jha, and Arman Rahmim, "What is implementation science; And why it matters for bridging the artificial intelligence innovation-to-application gap in medical imaging." arXiv [physics.med-ph], 2025/11/24 (2025).
23- Jason R. Frank et al., "Competency-based medical education: theory to practice." Med. Teach., Vol. 32 (No. 8), pp. 638-45, 2010 (2010).
24- A. Golzaryan, M. Soltani, F. Moradi Kashkooli, B. Saboury, A. Rahmim. "Personalized Metronomic Radiopharmaceutical Therapy through Injection Profile Optimization via Physiologically based Pharmacokinetic (PBPK) Modeling." Sci Rep, 15(1), p.4052 (2025).
25- V. V. Vikhman and M. V. Romm, "“digital twins” in education: Prospects and reality." Vysš. obraz. v Ross., Vol. 30 (No. 2), pp. 22-32, 2021/2/22 (2021).
26- Anahita Piranfar et al., "Radiopharmaceutical transport in solid tumors via a 3-dimensional image-based spatiotemporal model." NPJ Syst. Biol. Appl., Vol. 10 (No. 1), p. 39, 2024/4/12 (2024).
27- E. Z. Barsom, M. Graafland, and M. P. Schijven, "Systematic review on the effectiveness of augmented reality applications in medical training." Surg. Endosc., Vol. 30 (No. 10), pp. 4174-83, 2016/10 (2016).
28- Qian Li, Hui Duan, Xinxu Zhou, Xiaobin Sun, Lan Tao, and Xiuying Lu, "The use of metaverse in medical education: A systematic review." Clin. Med., Vol. 25 (No. 3), p. 100315, 2025/5 (2025).
29- Andrea Lastrucci et al., "Exploring augmented reality integration in diagnostic imaging: Myth or reality?" Diagnostics (Basel), Vol. 14 (No. 13), p. 1333, 2024/6/23 (2024).
30- Long Chen, Thomas W. Day, Wen Tang, and Nigel W. John, "Recent developments and future challenges in medical mixed reality." Presented at the 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2017/10, (2017). [Online]. Available:
31- Federico Nicolosi, Camilla de Laurentis, Carlo Giussani, Hani J. Marcus, Giannantonio Spena, and Ahmed Ammar, "Mixed reality and metaverse." in Ethical Challenges for the Future of Neurosurgery. Cham: Springer Nature Switzerland, (2024), pp. 141-59.
32- Xiaoge Zhang, Felix T. S. Chan, Chao Yan, and Indranil Bose, "Towards risk-aware artificial intelligence and machine learning systems: An overview." Decis. Support Syst., Vol. 159 (No. 113800), p. 113800, 2022/8 (2022).
33- Luca Marelli and Giuseppe Testa, "Scrutinizing the EU general data protection regulation." Science, Vol. 360 (No. 6388), pp. 496-98, 2018/5/4 (2018).
34- Kim Murphy-Abdouch, "Patient access to personal health information: Regulation vs. Reality." Perspect. Health Inf. Manag., Vol. 12p. 1c, 2015/1/1 (2015).
35- Xuan Gong et al., "Federated learning with privacy-preserving ensemble attention distillation." IEEE Trans. Med. Imaging, Vol. 42 (No. 7), pp. 2057-67, 2023/7 (2023).
36- Geoff Currie, K. Elizabeth Hawk, and Eric M. Rohren, "Ethical principles for the application of artificial intelligence (AI) in nuclear medicine." Eur. J. Nucl. Med. Mol. Imaging, Vol. 47 (No. 4), pp. 748-52, 2020/4 (2020).
37- Melissa D. McCradden et al., "A research ethics framework for the clinical translation of healthcare machine learning." Am. J. Bioeth., Vol. 22 (No. 5), pp. 8-22, 2022/5 (2022).
38- E. U. Parliament, "EU AI Act: first regulation on artificial Intelligence (2023)." 2024 (2024).
39- Jie Zhang and Zong-Ming Zhang, "Ethics and governance of trustworthy medical artificial intelligence." BMC Med. Inform. Decis. Mak., Vol. 23 (No. 1), p. 7, 2023/1/13 (2023).
40- Jonathan Birch, Kathleen A. Creel, Abhinav K. Jha, and Anya Plutynski, "Clinical decisions using AI must consider patient values." Nat. Med., Vol. 28 (No. 2), pp. 229-32, 2022/2 (2022).
| Files | ||
| Issue | Vol 13 No 3 (2026) | |
| Section | Literature (Narrative) Review(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i3.22757 | |
| Keywords | ||
| Theranostics Artificial Intelligence Digital Twins Medical Education Precision Oncology | ||
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How to Cite
1.
Abdollahi H, Fayaz-Bakhsh A, Klyuzhin I, Soltani M, Saboury B, Rahmim A. 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. Frontiers Biomed Technol. 2026;13(3):853-862.
