The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Digital twins to personalize medicine
Open accessAbstract
Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“In healthcare, digital twins are a promising tool for drug target discovery 65,66 .”
From Multimodal biomedical AI (2022) · cites this paper as [65]
The cited source says what the claim attributes to it.
In the citing paper
“This will likely require large collaborations across health systems, research groups and industry, such as the Swedish Digital Twins Consortium 65,72 .”
From Multimodal biomedical AI (2022) · cites this paper as [65]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Network medicine: a network-based approach to human disease
Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2918 · Cited by 5,034
Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis
Smillie, C. S., Biton, M., Ordovás-Montañés, J., Sullivan, K. M., Burgin, G., Graham, D. B., Herbst, R. H., Rogel, N., Slyper, M., Waldman, J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.06.029 · Cited by 1,417
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,288
Tao, F., & Qi, Q. (2019) · Nature · doi:10.1038/d41586-019-02849-1 · Cited by 799
Human symptoms–disease network
Zhou, X., Menche, J., Barabási, A.-Ĺ., & Sharma, A. (2014) · Nature Communications · doi:10.1038/ncomms5212 · Cited by 659
Single-cell analyses to tailor treatments
Shalek, A. K., & Benson, M. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aan4730 · Cited by 163
Self-citationA decade of digital medicine innovation
Topol, E. J. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw7610 · Cited by 117
Gawel, D. R., Serra-Musach, J., Lilja, S., Aagesen, J., Arenas, À., Asking, B., Bengnér, M., Björkander, J., Biggs, S., Ernerudh, J., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0657-3 · Cited by 95
Self-citationGustafsson, M., Gawel, D. R., Alfredsson, L. S., Baranzini, S. E., Björkander, J., Blomgran, R., Hellberg, S., Eklund, D., Ernerudh, J., Kockum, I. S., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aad2722 · Cited by 67
Self-citation
Research works printed but not linked: 1
- US Food and Drug Administration. Paving the way for personalized medicine: FDA’s role in a new era of medical product development. Silver Spring: US Food and Drug Administration; 2013. https://www.fdanews.com/ext/resources/files/10/10-28-13-Personalized-Medicine.pdf. Accessed 26 Nov 2019.
Cited by
Sorted by citations
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
Digital twins for health: a scoping review
Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L. E. K., Liu, H., Jackson, P., Xiao, Y., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01073-0 · Cited by 848
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616
How Machine Learning Will Transform Biomedicine
Goecks, J., Jalili, V., Heiser, L. M., & Gray, J. W. (2020) · Cell · doi:10.1016/j.cell.2020.03.022 · Cited by 604
The next generation of evidence-based medicine
Subbiah, V. (2023) · Nature Medicine · doi:10.1038/s41591-022-02160-z · Cited by 606
Digital Twins: From Personalised Medicine to Precision Public Health
Kamel Boulos, M. N., & Zhang, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11080745 · Cited by 530
Digital twin in healthcare: Recent updates and challenges
Sun, T., He, X., & Li, Z. (2023) · Digital Health · doi:10.1177/20552076221149651 · Cited by 421
Applications of Digital Twin across Industries: A Review
Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376
On the Integration of Agents and Digital Twins in Healthcare
Croatti, A., Gabellini, M., Montagna, S., & Ricci, A. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01623-5 · Cited by 356
Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare
Mohr, A. E., Ortega-Santos, C. P., Whisner, C. M., Klein-Seetharaman, J., & Jasbi, P. (2024) · Biomedicines · doi:10.3390/biomedicines12071496 · Cited by 311
Desai, N., Rana, D., Patel, M., Bajwa, N., Prasad, R., & Vora, L. K. (2025) · Small · doi:10.1002/smll.202502315 · Cited by 341
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301
Digital Twins for Multiple Sclerosis
Voigt, I., Inojosa, H., Dillenseger, A., Haase, R., Akgün, K., & Ziemssen, T. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.669811 · Cited by 294
Chen, J., Yi, C., Okegbile, S. D., Cai, J., & Shen, X. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3308717 · Cited by 284
Jia, W., Wang, W., & Zhang, Z. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101706 · Cited by 256
Laubenbacher, R., Mehrad, B., Shmulevich, I., & Trayanova, N. A. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00607-6 · Cited by 252
Digital twins of food process operations: the next step for food process models?
Verboven, P., Defraeye, T., Datta, A. K., & Nicolaï, B. M. (2020) · Current Opinion in Food Science · doi:10.1016/j.cofs.2020.03.002 · Cited by 226
The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks
Popa, E., van Hilten, M., Oosterkamp, E., & Bogaardt, M. (2021) · Life Sciences, Society and Policy · doi:10.1186/s40504-021-00113-x · Cited by 152
Digital twin–driven aero-engine intelligent predictive maintenance
Xiong, M., Wang, H., Fu, Q., & Xu, Y. M. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06976-w · Cited by 196
Sahal, R., Alsamhi, S. H., & Brown, K. N. (2022) · Italian National Conference on Sensors · doi:10.3390/s22155918 · Cited by 145
Domenico, M. D., Allegri, L., Caldarelli, G., d’Andrea, V., Camillo, B. D., Rocha, L. M., Rozum, J. C., Sbarbati, R., & Zambelli, F. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01402-3 · Cited by 196
Digital Twins: The New Frontier for Personalized Medicine?
Cellina, M. I., Cè, M., Alì, M., Irmici, G., Ibba, S., Caloro, E., Fazzini, D., Oliva, G., & Papa, S. (2023) · Applied Sciences · doi:10.3390/app13137940 · Cited by 178
Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins
Vallée, A. (2024) · Journal of Medical Internet Research · doi:10.2196/50204 · Cited by 154
Concepts and applications of digital twins in healthcare and medicine
Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 185
Benedictis, A. D., Mazzocca, N., Somma, A., & Strigaro, C. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3205506 · Cited by 158
Digital Twins’ Advancements and Applications in Healthcare, Towards Precision Medicine
Papachristou, K., Katsakiori, P. F., Papadimitroulas, P., Strigari, L., & Kagadis, G. C. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14111101 · Cited by 156
Yang, Y., Shi, Y., Yi, C., Cai, J., Kang, J., Niyato, D. T., & Shen, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3406607 · Cited by 149
Digital Twins in Healthcare: Methodological Challenges and Opportunities
Meijer, C., Uh, H., & el Bouhaddani, S. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13101522 · Cited by 122
The integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143
Generative-AI-Driven Human Digital Twin in IoT Healthcare: A Comprehensive Survey
Chen, J., Shi, Y., Yi, C., Du, H., Kang, J., & Niyato, D. T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3421918 · Cited by 168
Medical digital twins: enabling precision medicine and medical artificial intelligence
Sadée, C., Testa, S., Barba, T., Hartmann, K., Schuessler, M., Thieme, A. H., Church, G. M., Okoye, I. J., Hernandez‐Boussard, T., Hood, L., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.02.004 · Cited by 167
Application of Digital Twins in multiple fields
Guo, J., & Lv, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12536-5 · Cited by 151
Wu, C., Lorenzo, G., Hormuth, D. A., Lima, E. A. B. F., Slavkova, K. P., DiCarlo, J. C., Virostko, J. M., Phillips, C. M., Patt, D. A., Chung, C. D., et al. (2022) · Biophysics Reviews · doi:10.1063/5.0086789 · Cited by 147
Represent me: please! Towards an ethics of digital twins in medicine
Braun, M. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-106134 · Cited by 130
Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
Moztarzadeh, O., Jamshidi, M. B., Sargolzaei, S., Jamshidi, A., Baghalipour, N., Malekzadeh, M., & Hauer, L. (2023) · Bioengineering · doi:10.3390/bioengineering10040455 · Cited by 127
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124
The role of AI for developing digital twins in healthcare: The case of cancer care
Kaul, R., Ossai, C. I., Forkan, A. R. M., Jayaraman, P. P., Zelcer, J., Vaughan, S., & Wickramasinghe, N. S. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1480 · Cited by 125
Challenges and recommendations for the translation of biomarkers of aging
Herzog, C. M. S., Goeminne, L. J. E., Poganik, J. R., Barzilai, N., Belsky, D. W., Betts-LaCroix, J., Chen, B. H., Chen, M., Cohen, A. A., Cummings, S. R., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00683-3 · Cited by 119
Digital twins as global learning health and disease models for preventive and personalized medicine
Li, X., Loscalzo, J., Mahmud, A. K. M. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01435-7 · Cited by 135
Self-citationBuilding the metaverse using digital twins at all scales,states, and relations
Lv, Z., Xie, S., Li, Y., Hossain, M. S., & Saddik, A. E. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.06.005 · Cited by 108
A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems
Denecke, K., & Baudoin, C. R. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.795957 · Cited by 112
Immune digital twins for complex human pathologies: applications, limitations, and challenges
Niarakis, A., Laubenbacher, R., An, G. C., Ilan, Y., Fisher, J., Flobak, Å., Reiche, K., Martínez, M. R., Geris, L., Ladeira, L., et al. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00450-5 · Cited by 104
Precision nutrition: Maintaining scientific integrity while realizing market potential
Berciano, S., Figueiredo, J., Brisbois, T. D., Alford, S., Koecher, K. J., Eckhouse, S., Ciati, R., Kussmann, M., Ordovás, J. M., Stebbins, K., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.979665 · Cited by 95
Derraz, B., Breda, G., Kaempf, C., Baenke, F., Cotte, F., Reiche, K., Köhl, U., Kather, J. N., Eskenazy, D., & Gilbert, S. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00517-w · Cited by 94
A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse
Turab, M., & Jamil, S. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3030039 · Cited by 100
Digital twin applications on cultural world heritage sites in China: A state-of-the-art overview
Dang, X., Liu, W., Hong, Q., Wang, Y., & Chen, X. (2023) · Journal of Cultural Heritage · doi:10.1016/j.culher.2023.10.005 · Cited by 100
The Potential of a Digital Twin in Surgery
Ahmed, H., & Devoto, L. (2020) · Surgical Innovation · doi:10.1177/1553350620975896 · Cited by 88
Artificial intelligence in hematological diagnostics: Game changer or gadget?
Walter, W., Pohlkamp, C., Meggendorfer, M., Nadarajah, N., Kern, W., Haferlach, C., & Haferlach, T. (2022) · Blood Reviews · doi:10.1016/j.blre.2022.101019 · Cited by 93
Malone, B., Simovski, B., Moliné, C., Cheng, J., Gheorghe, M., Fontenelle, H., Vardaxis, I., Tennøe, S., Malmberg, J.-A., Stratford, R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78758-5 · Cited by 87
How artificial intelligence might disrupt diagnostics in hematology in the near future
Walter, W., Haferlach, C., Nadarajah, N., Schmidts, I., Kühn, C., Kern, W., & Haferlach, T. (2021) · Oncogene · doi:10.1038/s41388-021-01861-y · Cited by 93
Stavropoulos, P., & Mourtzis, D. (2022) · Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology · doi:10.1016/b978-0-12-823657-4.00010-5 · Cited by 80
Twin-S: a digital twin for skull base surgery
Shu, H., Liang, R., Li, Z., Goodridge, A., Zhang, X., Ding, H., Nagururu, N. V., Sahu, M., Creighton, F. X., Taylor, R. H., et al. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-02863-9 · Cited by 80
Digital twins in healthcare: a comprehensive review and future directions
Rudsari, H. K., Tseng, B., Zhu, H., Song, L., Gu, C., Roy, A., Irajizad, E., Butner, J. D., Long, J. P., & Do, K.-A. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1633539 · Cited by 105
Metaverse and Personal Healthcare
Song, Y.-T., & Qin, J. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.10.136 · Cited by 79
Digital twin for personalized medicine development
Saratkar, S., Langote, M., Kumar, P., Gote, P. M., Weerarathna, I. N., & Mishra, G. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1583466 · Cited by 88
Wickramasinghe, N. S., Jayaraman, P. P., Forkan, A. R. M., Ulapane, N. N. B., Kaul, R., Vaughan, S., & Zelcer, J. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3065381 · Cited by 75
Vallée, A. (2025) · Journal of Medical Internet Research · doi:10.2196/72411 · Cited by 86
Digital Twins for Healthcare Using Wearables
Johnson, Z., & Saikia, M. J. (2024) · Bioengineering · doi:10.3390/bioengineering11060606 · Cited by 74
A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients
Allen, A. O., Siefkas, A. C., Pellegrini, E., Burdick, H., Barnes, G., Calvert, J. S., Mao, Q., & Das, R. (2021) · Applied Sciences · doi:10.3390/app11125576 · Cited by 78
Latif, K., Sharafat, A., & Seo, J. (2023) · Applied Sciences · doi:10.3390/app132011435 · Cited by 78
Abdollahi, H., Yousefirizi, F., Shiri, I., Brosch-Lenz, J., Mollaheydar, E., Fele‐Paranj, A., Shi, K., Zaidi, H., Alberts, I., Soltani, M., et al. (2024) · Theranostics · doi:10.7150/thno.93973 · Cited by 71
Bhattad, P. B., & Jain, V. K. (2020) · Cureus · doi:10.7759/cureus.8041 · Cited by 73
Defining a Digital Twin: A Data Science-Based Unification
Emmert‐Streib, F. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030054 · Cited by 71
Kiran, M., Xie, Y., Anjum, N., Ball, G., Pierscionek, B. K., & Russell, D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1557467 · Cited by 89
Pervasive and Connected Digital Twins—A Vision for Digital Health
Ricci, A., Croatti, A., & Montagna, S. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3052039 · Cited by 65
Digital twins for the era of personalized surgery
Mekki, Y. M., Luijten, G., Hagert, E. M., Belkhair, S., Varghese, C., Qadir, J., Solaiman, B., Bilal, M., Dhanda, J., Egger, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01575-5 · Cited by 65
Developments of digital twin technologies in industrial, smart city and healthcare sectors: a survey
Yang, D., Karimi, H. R., Kaynak, O., & Yin, S. (2021) · Complex Engineering Systems · doi:10.20517/ces.2021.06 · Cited by 65
Mulder, S. T., Omidvari, A., Rueten‐Budde, A. J., Huang, P., Kim, K.-H., Bais, B., Rousian, M., Hai, R., Akgün, C., van Lennep, J. E. R., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/35675 · Cited by 64
Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook
Liu, W., Wu, M., Wan, G., & Xu, M. (2024) · Remote Sensing · doi:10.3390/rs16163023 · Cited by 71
A comprehensive review of digital twin in healthcare in the scope of simulative health-monitoring
Nadeem, M., Kostic, S., Dornhöfer, M., Weber, C., & Fathi, M. (2025) · Digital Health · doi:10.1177/20552076241304078 · Cited by 71
Digital Twins for Tissue Culture Techniques—Concepts, Expectations, and State of the Art
Möller, J., & Pörtner, R. (2021) · Processes · doi:10.3390/pr9030447 · Cited by 63
Zsidai, B., Hilkert, A., Kaarre, J., Narup, E., Senorski, E. H., Grassi, A., Ley, C., Longo, U. G., Herbst, E., Hirschmann, M. T., et al. (2023) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-023-00683-z · Cited by 71
Batch, K., Yue, J., Darcovich, A., Lupton, K., Liu, C. C., Woodlock, D. P., Amine, M. A. E., Andrieu, P. C., Gazit, L., Nguyen, G. H., et al. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.826402 · Cited by 65
Digital twin systems for musculoskeletal applications: A current concepts review
Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R. (2025) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1002/ksa.12627 · Cited by 60
A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets
Li, X., Lee, E. J., Lilja, S., Loscalzo, J., Schäfer, S., Smelik, M., Strobl, M. R., Sysoev, O., Wang, H., Zhang, H., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01048-4 · Cited by 60
Self-citationUnlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
Mariam, Z., Niazi, S. K., & Magoola, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020079 · Cited by 61
Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma
Branco, F., Cunha, J., Mendes, M., Vitorino, C., & Sousa, J. (2024) · ACS Nano · doi:10.1021/acsnano.4c01790 · Cited by 60
Digital Twins in Personalized Medicine: Bridging Innovation and Clinical Reality
Silva, A., & Vale, N. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15110503 · Cited by 78
An, G. C., & Cockrell, C. (2022) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.928387 · Cited by 57
The potential of the Medical Digital Twin in diabetes management: a review
Chu, Y., Li, S., Tang, J., & Wu, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1178912 · Cited by 57
Hormuth, D. A., Phillips, C. M., Wu, C., Lima, E. A. B. F., Lorenzo, G., Jha, P. K., Jarrett, A. M., Oden, J. T., & Yankeelov, T. E. (2021) · Cancers · doi:10.3390/cancers13123008 · Cited by 62
Zhong, X., Sarijaloo, F. B., Prakash, A., Park, J., Huang, C., Barwise, A., Herasevich, V., Gajic, O., Pickering, B., & Dong, Y. (2022) · International Journal of Production Research · doi:10.1080/00207543.2021.2022235 · Cited by 42
Yazdani, E., Geramifar, P., Ziarati, N. K., Sadeghi, M., Amini, P., & Rahmim, A. (2024) · Diagnostics · doi:10.3390/diagnostics14020181 · Cited by 55
Digital patient twins for personalized therapeutics and pharmaceutical manufacturing
Fischer, R.-P., Volpert, A., Antonino, P. O., & Ahrens, T. D. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1302338 · Cited by 59
Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review
Ştefănigă, S.-A., Cordoș, A., Ivaşcu, T., Feier, C. V. I., Muntean, C., Stupinean, C. V., Călinici, T., Aluaș, M., & Bolboaca, S. D. (2024) · Cancers · doi:10.3390/cancers16223817 · Cited by 61
Digital twins to enable better precision and personalized dementia care
Wickramasinghe, N. S., Ulapane, N. N. B., Andargoli, A., Ossai, C. I., Shuakat, M. N., Nguyen, T. A., & Zelcer, J. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac072 · Cited by 52
Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring
Zhu, L., Lu, W., Soleimani, M., Li, Z., & Zhang, M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298389 · Cited by 51
Kirienko, M., Sollini, M., Ninatti, G., Loiacono, D., Giacomello, E., Gozzi, N., Francesco Amigoni, A., Mainardi, L. T., Lanzi, P. L., & Chiti, A. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05339-7 · Cited by 51
Medical Digital Twin: A Review on Technical Principles and Clinical Applications
Tortora, M., Pacchiano, F., Ferraciolli, S. F., Criscuolo, S., Gagliardo, C., Jaber, K., Angelicchio, M., Briganti, F., Caranci, F., Tortora, F., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020324 · Cited by 58
Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation
Falkowski, P., Osiak, T., Wilk, J., Prokopiuk, N., Leczkowski, B., Pilat, Z., & Rzymkowski, C. (2023) · Sensors · doi:10.3390/s23020911 · Cited by 49
Roesch, E., Greener, J. G., MacLean, A. L., Nassar, H., Rackauckas, C., Holy, T. E., & Stumpf, M. P. H. (2023) · Nature Methods · doi:10.1038/s41592-023-01832-z · Cited by 55
Roopa, M. S., & Venugopal, K. R. (2025) · IEEE Access · doi:10.1109/access.2025.3547991 · Cited by 56
Rajendran, S., Pan, W., Sabuncu, M. R., Chen, Y., Zhou, J., & Wang, F. (2024) · Patterns · doi:10.1016/j.patter.2023.100913 · Cited by 47
Machine Learning in Clinical Trials: A Primer with Applications to Neurology
Miller, M. I., Shih, L. C., & Kolachalama, V. B. (2023) · Neurotherapeutics · doi:10.1007/s13311-023-01384-2 · Cited by 58
Montagud, A., Ponce-de-León, M., & Valencia, A. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.100385 · Cited by 46
Introduction of a comprehensive Structure Model for the Digital Twin in Manufacturing
Lechler, T., Fuchs, J., Sjarov, M., Brossog, M., Selmaier, A., Faltus, F., Donhauser, T., & Franke, J. E. (2020) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa46521.2020.9212030 · Cited by 47
Khan, S., Alzaabi, A., Iqbal, Z., Ratnarajah, T., & Arslan, T. (2023) · IEEE Access · doi:10.1109/access.2023.3316508 · Cited by 55
Digital twins: a new paradigm in oncology in the era of big data
Mollica, L., Leli, C., Sottotetti, F., Quaglini, S., Locati, L. D., & Marceglia, S. R. F. (2024) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2024.100056 · Cited by 45
Andres, A., Roland, M., Wickert, K., Diebels, S., Stöckl, J., Herrmann, S., Reinauer, F., Leibinger, R., Pavlov, A., Schuppener, L., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-04792-w · Cited by 45
Digital twins: potentials, ethical issues and limitations
Helbing, D., & Sánchez–Vaquerizo, J. A. (2023) · Edward Elgar Publishing eBooks · doi:10.4337/9781800887374.00013 · Cited by 70
Metadata
- Authors
- on behalf of the Swedish Digital Twin Consortium, Björnsson, B., Borrebaeck, C., Elander, N., Gasslander, T., Gawel, D. R., Gustafsson, M., Jörnsten, R., Lee, E. J., Li, X., Lilja, S., Martínez-Enguita, D., Matussek, A., Sandström, P., Schäfer, S., Stenmarker, M., Sun, X. F., Sysoev, O., Zhang, H., & Benson, M.
- Affiliations
- Lund University (SE)
- Linköping University (SE)
- Chalmers University of Technology (SE)
- Karolinska University Hospital (SE)
- Futurum - Akademin för Hälsa och Vård (SE)
- Corresponding author
- Benson, Mikael
- ORCID
- Venue
- Genome Medicine
- Published
- 2019-12-31
- Volume
- 12
- Issue
- 1
- Pages
- 4-4
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC6938608
- OpenAlex
- W2999365564
- Cited by
- 566
- References
- 9
- Citations by year
- 2026: 143 · 2025: 126 · 2024: 103 · 2023: 79 · 2022: 66 · 2021: 37 · 2020: 12
- FWCI
- 19.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lund University Publications (Lund University) (submittedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Swedish Cancer Foundation
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- digital twin, personalized medicine, patient modeling, drug repurposing, computational drug screening, precision medicine, data integration
- MeSH terms
- Disease, Humans, Databases, Factual, Neural Networks, Computer, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
