Abstract
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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Acknowledgements
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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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). https://doi.org/10.1038/s43588-024-00607-6
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DOI: https://doi.org/10.1038/s43588-024-00607-6