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  • Perspective
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Digital twins in medicine


Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

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Fig. 1: Applications for medical digital twins.

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  1. The Precision Medicine Initiative Cohort Program—Building a Research Foundation for 21st Century Medicine (NIH, 2015);

  2. Eddy, D. M. & Schlessinger, L. Archimedes: a trial-validated model of diabetes. Diabetes Care 26, 3093–3101 (2003).

    Article  Google Scholar 

  3. Tomczak, K., Czerwińska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. Pozn. Pol. 19, A68–A77 (2015).

    Google Scholar 

  4. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  Google Scholar 

  5. Alber, M. et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical and behavioral sciences. NPJ Digit. Med. 2, 115 (2019).

    Article  Google Scholar 

  6. Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422–440 (2021).

    Article  Google Scholar 

  7. Opportunities and Challenges for Digital Twins in Biomedical Research (National Academies of Science, Engineering and Medicine, 2023);

  8. Opportunities and Challenges for Digital Twins in Atmospheric and Climate Sciences: Proceedings of a Workshop—In Brief 26921 (National Academies Press, 2023);

  9. Foundational Research Gaps and Future Directions for Digital Twins (National Academies of Sciences, Engineering and Medicine, 2023);

  10. Wright, L. & Davidson, S. How to tell the difference between a model and a digital twin. Adv. Model. Simul. Eng. Sci. 7, 13 (2020).

    Article  Google Scholar 

  11. Vogelsang, A. & Borg, M. Requirements engineering for machine learning: perspectives from data scientists. In Proc. 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (eds Damian, D., Perini, A. & Lee, S.-W.) 245–251 (IEEE, 2019);

  12. Cobelli, C. & Kovatchev, B. Developing the UVA/Padova type 1 diabetes simulator: modeling, validation, refinements and utility. J. Diabetes Sci. Technol. (2023).

  13. Breton, M. D. et al. A randomized trial of closed-loop control in children with Type 1 diabetes. N. Engl. J. Med. 383, 836–845 (2020).

    Article  Google Scholar 

  14. Quintairos, A., Pilcher, D. & Salluh, J. I. F. ICU scoring systems. Intensive Care Med. 49, 223–225 (2023).

    Article  Google Scholar 

  15. Dang, J. et al. Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit. BMC Neurol. 23, 161 (2023).

    Article  Google Scholar 

  16. Lal, A. et al. Development and verification of a digital twin patient model to predict specific treatment response during the first 24 hours of sepsis. Crit. Care Explor. 2, e0249 (2020).

    Article  Google Scholar 

  17. Cockrell, C. & An, G. Sepsis reconsidered: identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model. J. Theor. Biol. 430, 157–168 (2017).

    Article  Google Scholar 

  18. Larie, D., An, G. & Cockrell, R. C. The use of artificial neural networks to forecast the behavior of agent-based models of pathophysiology: an example utilizing an agent-based model of sepsis. Front. Physiol. 12, 716434 (2021).

    Article  Google Scholar 

  19. Ribeiro, H. A. et al. Multi-scale mechanistic modelling of the host defence in invasive aspergillosis reveals leucocyte activation and iron acquisition as drivers of infection outcome. J. R. Soc. Interface 19, 20210806 (2022).

    Article  Google Scholar 

  20. Chasseloup, E., Hooker, A. C. & Karlsson, M. O. Generation and application of avatars in pharmacometric modelling. J. Pharmacokinet. Pharmacodyn. 50, 411–423 (2023).

    Article  Google Scholar 

  21. Moingeon, P., Chenel, M., Rousseau, C., Voisin, E. & Guedj, M. Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials. Drug Discov. Today 28, 103605 (2023).

    Article  Google Scholar 

  22. Allen, R., Rieger, T. & Musante, C. Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacomet. Syst. Pharmacol. 5, 140–146 (2016).

    Article  Google Scholar 

  23. Hernandez-Boussard, T. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat. Med. 27, 2065–2066 (2021).

    Article  Google Scholar 

  24. Stahlberg, E. A. et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Front. Digit. Health 4, 1007784 (2022).

    Article  Google Scholar 

  25. Wu, C. et al. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys. Rev. 3, 021304 (2022).

    Article  Google Scholar 

  26. Jarrett, A. M. et al. Optimal control theory for personalized therapeutic regimens in oncology: background, history, challenges and opportunities. J. Clin. Med. 9, 1314 (2020).

    Article  Google Scholar 

  27. Baldock, A. L. et al. From patient-specific mathematical neuro-oncology to precision medicine. Front. Oncol. 3, 62 (2013).

    Article  Google Scholar 

  28. Jackson, P. R., Juliano, J., Hawkins-Daarud, A., Rockne, R. C. & Swanson, K. R. Patient-specific mathematical neuro-oncology: using a simple proliferation and invasion tumor model to inform clinical practice. Bull. Math. Biol. 77, 846–856 (2015).

    Article  MathSciNet  Google Scholar 

  29. Hawkins-Daarud, A. et al. In silico analysis suggests differential response to bevacizumab and radiation combination therapy in newly diagnosed glioblastoma. J. R. Soc. Interface 12, 20150388 (2015).

    Article  Google Scholar 

  30. Arevalo, H. J. et al. Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat. Commun. 7, 11437 (2016).

    Article  Google Scholar 

  31. Sung, E. et al. Fat infiltration in the infarcted heart as a paradigm for ventricular arrhythmias. Nat. Cardiovasc. Res. 1, 933–945 (2022).

    Article  Google Scholar 

  32. Cartoski, M. J. et al. Computational identification of ventricular arrhythmia risk in pediatric myocarditis. Pediatr. Cardiol. 40, 857–864 (2019).

    Article  Google Scholar 

  33. Shade, J. K. et al. Ventricular arrhythmia risk prediction in repaired tetralogy of Fallot using personalized computational cardiac models. Heart Rhythm 17, 408–414 (2020).

    Article  Google Scholar 

  34. O’Hara, R. P. et al. Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy. eLife 11, e73325 (2022).

    Article  Google Scholar 

  35. Shade, J. K. et al. Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier. Sci. Adv. 7, eabi8020 (2021).

    Article  Google Scholar 

  36. Zhang, Y. et al. Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins. eLife 10.7554/eLife.88865.2 (2023).

  37. Ashikaga, H. et al. Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia. Heart Rhythm 10, 1109–1116 (2013).

    Article  Google Scholar 

  38. Prakosa, A. et al. Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat. Biomed. Eng. 2, 732–740 (2018).

    Article  Google Scholar 

  39. Sung, E. et al. Personalized digital-heart technology for ventricular tachycardia ablation targeting in hearts with infiltrating adiposity. Circ. Arrhythm. Electrophysiol. 13, e008912 (2020).

    Article  Google Scholar 

  40. McDowell, K. S. et al. Virtual electrophysiological study of atrial fibrillation in fibrotic remodeling. PLoS ONE 10, e0117110 (2015).

    Article  Google Scholar 

  41. Roney, C. H. et al. In silico comparison of left atrial ablation techniques that target the anatomical, structural and electrical substrates of atrial fibrillation. Front. Physiol. 11, 1145 (2020).

    Article  Google Scholar 

  42. Zahid, S. et al. Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc. Res. 110, 443–454 (2016).

    Article  Google Scholar 

  43. Loewe, A. et al. Patient-specific identification of atrial flutter vulnerability—a computational approach to reveal latent reentry pathways. Front. Physiol. 9, 1910 (2019).

    Article  Google Scholar 

  44. Roney, C. H. et al. Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models. Circ. Arrhythm. Electrophysiol. 15, e010253 (2022).

    Article  Google Scholar 

  45. Boyle, P. M. et al. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat. Biomed. Eng. 3, 870–879 (2019).

    Article  Google Scholar 

  46. Ali, R. L. et al. Arrhythmogenic propensity of the fibrotic substrate after atrial fibrillation ablation: a longitudinal study using magnetic resonance imaging-based atrial models. Cardiovasc. Res. 115, 1757–1765 (2019).

    Article  Google Scholar 

  47. Shade, J. K. et al. Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation. Circ. Arrhythm. Electrophysiol. 13, e008213 (2020).

    Article  Google Scholar 

  48. EDITH: European Virtual Human Twin (Virtual Physiological Human Institute);

  49. Viceconti, M. & Hunter, P. The virtual physiological human: ten years after. Annu. Rev. Biomed. Eng. 18, 103–123 (2016).

    Article  Google Scholar 

  50. Swedish Digital Twin Consortium (SDTC);

  51. Björnsson, B. et al. Digital twins to personalize medicine. Genome Med. 12, 4 (2019).

    Article  Google Scholar 

  52. Laubenbacher, R., Sluka, J. P. & Glazier, J. A. Using digital twins in viral infection. Science 371, 1105–1106 (2021).

    Article  Google Scholar 

  53. Laubenbacher, R. et al. Building digital twins of the human immune system: toward a roadmap. NPJ Digit. Med. 5, 64 (2022).

    Article  Google Scholar 

  54. Forum on Precision Immunology: Immune Digital Twins (UF Laboratory for Systems Medicine);

  55. Building Immune Digital Twins (Institut Pascal);

  56. EDITH CSA Deliverable 3.2: First Draft of the VHT Roadmap (EDITH Consortium, 2023);

  57. Gartner 2018 Hype Cycle for IT in GCC Identifies Six Technologies That Will Reach Mainstream Adoption in Five to 10 Years (Gartner, 2018);

  58. Fitzgerald, J., Larsen, P. G., Margaria, T., Woodcock, J. & Gomes, C. in Leveraging Applications of Formal Methods, Verification and Validation, Lecture Notes in Computer Science (eds Margaria, T. & Steffen, B.) 13704 (Springer, 2022);

  59. Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions; Draft Guidance for Industry and Food and Drug Administration Staff (FDA, 2021);

  60. Nuwer, R. US agency seeks to phase out animal testing. Nature (2022).

  61. Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices (The American Society of Mechanical Engineers, 2018).

  62. Ahmed, K. B. R., Pathmanathan, P., Kabadi, S. V., Drgon, T. & Morrison, T. M. Editorial on the FDA report on ‘Successes and opportunities in modeling & simulation for FDA’. Ann. Biomed. Eng. 51, 6–9 (2023).

    Article  Google Scholar 

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R.L. acknowledges support from the US Army (ACC- APG- RTP W911NF), the National Institutes of Health (NIH) 1 R01 HL169974-01), the US Department of Defense DARPA (HR00112220038), NIH 1 R011 AI135128-01 and NIH 1 R01 HL169974-01. B.M. acknowledges support from NIH 1 R01 HL169974-01, NIH 1 R011 AI135128-01 and NIH 1 R01 HL169974-01. N.T. acknowledges support from NIH R01HL166759 and R01HL142496. I.S. acknowledges support from NIH NCI R01CA270210.

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The authors are listed in alphabetical order in the author list. R.L. conceived the Perspective and drafted the first outline. N.T. wrote the section on digital twins in cardiology. B.M. and I.S. contributed to the other sections. All authors reviewed and edited the final version.

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Correspondence to R. Laubenbacher.

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Nature Computational Science thanks A. M. Alaa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Laubenbacher, R., Mehrad, B., Shmulevich, I. et al. Digital twins in medicine. Nat Comput Sci 4, 184–191 (2024).

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