{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T23:15:32Z","timestamp":1794266132660,"version":"4.9.0"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Rev Urol"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1038\/s41585-025-01096-6","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T10:52:04Z","timestamp":1760093524000},"page":"29-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Digital twins for personalized treatment in uro-oncology in the era of artificial intelligence"],"prefix":"10.1038","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6195-1609","authenticated-orcid":false,"given":"Magdalena","family":"G\u00f6rtz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Brandl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5891-4298","authenticated-orcid":false,"given":"Anna","family":"Nitschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7567-8153","authenticated-orcid":false,"given":"Anja","family":"Riediger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Stromer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7004-1586","authenticated-orcid":false,"given":"Michael","family":"Byczkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Heuveline","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5639-5126","authenticated-orcid":false,"given":"Matthias","family":"Weidem\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"1096_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1038\/s41746-022-00640-7","volume":"5","author":"G Coorey","year":"2022","unstructured":"Coorey, G. et al. The health digital twin to tackle cardiovascular disease \u2014 a review of an emerging interdisciplinary field. npj Digit. Med. 5, 126 (2022).","journal-title":"npj Digit. Med."},{"key":"1096_CR2","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1159\/000502000","volume":"3","author":"LM Babrak","year":"2019","unstructured":"Babrak, L. M. et al. Traditional and digital biomarkers: two worlds apart? Digit. Biomark. 3, 92\u2013102 (2019).","journal-title":"Digit. Biomark."},{"key":"1096_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.medengphy.2019.08.007","volume":"72","author":"DR Hose","year":"2019","unstructured":"Hose, D. R. et al. Cardiovascular models for personalised medicine: where now and where next? Med. Eng. Phys. 72, 38\u201348 (2019).","journal-title":"Med. Eng. Phys."},{"key":"1096_CR4","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221149651","volume":"9","author":"T Sun","year":"2023","unstructured":"Sun, T., He, X. & Li, Z. Digital twin in healthcare: recent updates and challenges. Digit. Health 9, 20552076221149651 (2023).","journal-title":"Digit. Health"},{"key":"1096_CR5","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","volume":"40","author":"J Lipkova","year":"2022","unstructured":"Lipkova, J. et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell 40, 1095\u20131110 (2022).","journal-title":"Cancer Cell"},{"key":"1096_CR6","unstructured":"Geris, L. EDITH \u2014 Building the European Virtual Human Twin. EDITH https:\/\/www.edith-csa.eu\/wp-content\/uploads\/2023\/04\/EDITH_D3.1_vision_roadmap_outline_final.pdf (2024)."},{"key":"1096_CR7","doi-asserted-by":"publisher","first-page":"745","DOI":"10.3390\/jpm11080745","volume":"11","author":"MN Kamel Boulos","year":"2021","unstructured":"Kamel Boulos, M. N. & Zhang, P. Digital twins: from personalised medicine to precision public health. J. Pers. Med. 11, 745 (2021).","journal-title":"J. Pers. Med."},{"key":"1096_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2023.1260361","volume":"6","author":"HC Chang","year":"2023","unstructured":"Chang, H. C. et al. Understanding the need for digital twins\u2019 data in patient advocacy and forecasting oncology. Front. Artif. Intell. 6, 1260361 (2023).","journal-title":"Front. Artif. Intell."},{"key":"1096_CR9","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1038\/s41591-021-01558-5","volume":"27","author":"T Hernandez-Boussard","year":"2021","unstructured":"Hernandez-Boussard, T. et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat. Med. 27, 2065\u20132066 (2021).","journal-title":"Nat. Med."},{"key":"1096_CR10","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.eururo.2016.08.024","volume":"71","author":"CE Barbieri","year":"2017","unstructured":"Barbieri, C. E., Chinnaiyan, A. M., Lerner, S. P., Swanton, C. & Rubin, M. A. The Emergence of precision urologic oncology: a collaborative review on biomarker-driven therapeutics. Eur. Urol. 71, 237\u2013246 (2017).","journal-title":"Eur. Urol."},{"key":"1096_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41746-022-00613-w","volume":"5","author":"A Esteva","year":"2022","unstructured":"Esteva, A. et al. Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. npj Digit. Med. 5, 71 (2022).","journal-title":"npj Digit. Med."},{"key":"1096_CR12","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s43018-025-00991-6","volume":"6","author":"D Ferber","year":"2025","unstructured":"Ferber, D. et al. Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology. Nat. Cancer 6, 1337\u20131349\u00a0(2025).","journal-title":"Nat. Cancer"},{"key":"1096_CR13","unstructured":"Poon H. Multimodal generative AI: the next frontier in precision health. Microsoft Research Forum Brief March 2024 https:\/\/www.microsoft.com\/en-us\/research\/articles\/multimodal-generative-ai-the-next-frontier-in-precision-health\/ (updated 30 May 2025)."},{"key":"1096_CR14","unstructured":"European Commission. European Virtual Human Twins Initiative https:\/\/digital-strategy.ec.europa.eu\/en\/policies\/virtual-human-twins (updated 5 September 2025)."},{"key":"1096_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.euo.2024.10.005","volume":"8","author":"E Checcucci","year":"2025","unstructured":"Checcucci, E., Oing, C., Amparore, D., Porpiglia, F. & Rescigno, P. Digital twins in urological oncology: precise treatment planning via complex modeling. Eur. Urol. Oncol. 8, 1\u20134 (2025).","journal-title":"Eur. Urol. Oncol."},{"key":"1096_CR16","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2023.1253050","volume":"5","author":"A Vallee","year":"2023","unstructured":"Vallee, A. Digital twin for healthcare systems. Front. Digit. Health 5, 1253050 (2023).","journal-title":"Front. Digit. Health"},{"key":"1096_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-55228-w","volume":"14","author":"O Eminaga","year":"2024","unstructured":"Eminaga, O. et al. Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology. Sci. Rep. 14, 5284 (2024).","journal-title":"Sci. Rep."},{"key":"1096_CR18","first-page":"229","volume":"74","author":"F Bray","year":"2024","unstructured":"Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229\u2013263 (2024).","journal-title":"CA Cancer J. Clin."},{"key":"1096_CR19","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1016\/j.cell.2015.10.025","volume":"163","author":"Cancer Genome Atlas Research, N","year":"2015","unstructured":"Cancer Genome Atlas Research, N The molecular taxonomy of primary prostate cancer. Cell 163, 1011\u20131025 (2015).","journal-title":"Cell"},{"key":"1096_CR20","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1038\/s41585-020-00400-w","volume":"18","author":"MC Haffner","year":"2021","unstructured":"Haffner, M. C. et al. Genomic and phenotypic heterogeneity in prostate cancer. Nat. Rev. Urol. 18, 79\u201392 (2021).","journal-title":"Nat. Rev. Urol."},{"key":"1096_CR21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1038\/s41585-021-00524-7","volume":"19","author":"A Ali","year":"2022","unstructured":"Ali, A. et al. Prostate zones and cancer: lost in transition? Nat. Rev. Urol. 19, 101\u2013115 (2022).","journal-title":"Nat. Rev. Urol."},{"key":"1096_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2024.1421591","volume":"15","author":"A Perez-Benito","year":"2024","unstructured":"Perez-Benito, A., Garcia-Aznar, J. M., Gomez-Benito, M. J. & Perez, M. A. Patient-specific prostate tumour growth simulation: a first step towards the digital twin. Front. Physiol. 15, 1421591 (2024).","journal-title":"Front. Physiol."},{"key":"1096_CR23","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1158\/2767-9764.CRC-23-0449","volume":"4","author":"G Lorenzo","year":"2024","unstructured":"Lorenzo, G. et al. A pilot study on patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model. Cancer Res. Commun. 4, 617\u2013633 (2024).","journal-title":"Cancer Res. Commun."},{"key":"1096_CR24","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/s41746-025-01471-y","volume":"8","author":"E Fountzilas","year":"2025","unstructured":"Fountzilas, E., Pearce, T., Baysal, M. A., Chakraborty, A. & Tsimberidou, A. M. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. npj Digit. Med. 8, 75 (2025).","journal-title":"npj Digit. Med."},{"key":"1096_CR25","doi-asserted-by":"publisher","first-page":"e87","DOI":"10.1097\/PAS.0000000000001497","volume":"44","author":"G van Leenders","year":"2020","unstructured":"van Leenders, G. et al. The 2019 international society of urological pathology (ISUP) consensus conference on grading of prostatic carcinoma. Am. J. Surg. Pathol. 44, e87\u2013e99 (2020).","journal-title":"Am. J. Surg. Pathol."},{"key":"1096_CR26","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1097\/PAS.0000000000000530","volume":"40","author":"JI Epstein","year":"2016","unstructured":"Epstein, J. I. et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am. J. Surg. Pathol. 40, 244\u2013252 (2016).","journal-title":"Am. J. Surg. Pathol."},{"key":"1096_CR27","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","volume":"69","author":"JC Weinreb","year":"2016","unstructured":"Weinreb, J. C. et al. PI-RADS prostate imaging \u2014 reporting and data system: 2015, version 2. Eur. Urol. 69, 16\u201340 (2016).","journal-title":"Eur. Urol."},{"key":"1096_CR28","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","volume":"76","author":"B Turkbey","year":"2019","unstructured":"Turkbey, B. et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur. Urol. 76, 340\u2013351 (2019).","journal-title":"Eur. Urol."},{"key":"1096_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-66870-9","volume":"14","author":"GY Wang","year":"2024","unstructured":"Wang, G. Y. et al. Prediction of non-muscle invasive bladder cancer recurrence using deep learning of pathology image. Sci. Rep. 14, 18931 (2024).","journal-title":"Sci. Rep."},{"key":"1096_CR30","doi-asserted-by":"publisher","first-page":"024506","DOI":"10.1117\/1.JMI.4.2.024506","volume":"4","author":"N Lay","year":"2017","unstructured":"Lay, N. et al. Detection of prostate cancer in multiparametric MRI using random forest with instance weighting. J. Med. Imaging 4, 024506 (2017).","journal-title":"J. Med. Imaging"},{"key":"1096_CR31","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.eururo.2024.08.025","volume":"87","author":"S Figiel","year":"2025","unstructured":"Figiel, S. et al. Clinical implications of basic research: exploring the transformative potential of spatial \u2018omics in uro-oncology. Eur. Urol. 87, 8\u201314 (2025).","journal-title":"Eur. Urol."},{"key":"1096_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41540-023-00301-9","volume":"9","author":"RD Bulow","year":"2023","unstructured":"Bulow, R. D., Holscher, D. L., Costa, I. G. & Boor, P. Extending the landscape of omics technologies by pathomics. npj Syst. Biol. Appl. 9, 38 (2023).","journal-title":"npj Syst. Biol. Appl."},{"key":"1096_CR33","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/s41746-024-01402-3","volume":"8","author":"M De Domenico","year":"2025","unstructured":"De Domenico, M. et al. Challenges and opportunities for digital twins in precision medicine from a complex systems perspective. npj Digit. Med. 8, 37 (2025).","journal-title":"npj Digit. Med."},{"key":"1096_CR34","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1038\/s41568-021-00408-3","volume":"22","author":"KM Boehm","year":"2022","unstructured":"Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J. & Shah, S. P. Harnessing multimodal data integration to advance precision oncology. Nat. Rev. Cancer 22, 114\u2013126 (2022).","journal-title":"Nat. Rev. Cancer"},{"key":"1096_CR35","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1038\/s41575-020-0343-3","volume":"17","author":"JN Kather","year":"2020","unstructured":"Kather, J. N. & Calderaro, J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nat. Rev. Gastroenterol. Hepatol. 17, 591\u2013592 (2020).","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"1096_CR36","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-019-0701-3","volume":"12","author":"B Bjornsson","year":"2019","unstructured":"Bjornsson, B. et al. Digital twins to personalize medicine. Genome Med. 12, 4 (2019).","journal-title":"Genome Med."},{"key":"1096_CR37","doi-asserted-by":"publisher","DOI":"10.2196\/69544","volume":"27","author":"M Ringeval","year":"2025","unstructured":"Ringeval, M., Etindele Sosso, F. A., Cousineau, M. & Pare, G. Advancing health care with digital twins: meta-review of applications and implementation challenges. J. Med. Internet Res. 27, e69544 (2025).","journal-title":"J. Med. Internet Res."},{"key":"1096_CR38","doi-asserted-by":"publisher","first-page":"025010","DOI":"10.1088\/1361-6560\/ada684","volume":"70","author":"CW Chang","year":"2025","unstructured":"Chang, C. W. et al. Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins. Phys. Med. Biol. 70, 025010 (2025).","journal-title":"Phys. Med. Biol."},{"key":"1096_CR39","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2025.1434739","volume":"16","author":"G Stamatakos","year":"2025","unstructured":"Stamatakos, G. et al. In silico oncology: a mechanistic multiscale model of clinical prostate cancer response to external radiation therapy as the core of a digital (virtual) twin. Sensitivity analysis and a clinical adaptation approach. Front. Physiol. 16, 1434739 (2025).","journal-title":"Front. Physiol."},{"key":"1096_CR40","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.3601","volume":"38","author":"H Ahmadian","year":"2022","unstructured":"Ahmadian, H. et al. Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response. Int. J. Numer. Method. Biomed. Eng. 38, e3601 (2022).","journal-title":"Int. J. Numer. Method. Biomed. Eng."},{"key":"1096_CR41","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1111\/j.1464-410X.2005.05946.x","volume":"96","author":"D Petrylak","year":"2005","unstructured":"Petrylak, D. Therapeutic options in androgen-independent prostate cancer: building on docetaxel. BJU Int. 96, 41\u201346 (2005).","journal-title":"BJU Int."},{"key":"1096_CR42","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1038\/s41585-018-0017-x","volume":"15","author":"G Pignot","year":"2018","unstructured":"Pignot, G. et al. Systemic treatments for high-risk localized prostate cancer. Nat. Rev. Urol. 15, 498\u2013510 (2018).","journal-title":"Nat. Rev. Urol."},{"key":"1096_CR43","doi-asserted-by":"publisher","first-page":"19701","DOI":"10.1073\/pnas.1115750108","volume":"108","author":"HV Jain","year":"2011","unstructured":"Jain, H. V., Clinton, S. K., Bhinder, A. & Friedman, A. Mathematical modeling of prostate cancer progression in response to androgen ablation therapy. Proc. Natl Acad. Sci. USA 108, 19701\u201319706 (2011).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1096_CR44","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41746-024-01073-0","volume":"7","author":"E Katsoulakis","year":"2024","unstructured":"Katsoulakis, E. et al. Digital twins for health: a scoping review. npj Digit. Med. 7, 77 (2024).","journal-title":"npj Digit. Med."},{"key":"1096_CR45","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.3390\/jpm13081255","volume":"13","author":"P Gazerani","year":"2023","unstructured":"Gazerani, P. Intelligent digital twins for personalized migraine care. J. Pers. Med. 13, 1255 (2023).","journal-title":"J. Pers. Med."},{"key":"1096_CR46","doi-asserted-by":"crossref","unstructured":"Koskim\u00e4ki, H. et al. in Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 506\u2013511.","DOI":"10.1145\/3123024.3124434"},{"key":"1096_CR47","doi-asserted-by":"publisher","DOI":"10.2196\/23229","volume":"5","author":"SH Ingvaldsen","year":"2021","unstructured":"Ingvaldsen, S. H. et al. A biofeedback App for migraine: development and usability study. JMIR Form. Res. 5, e23229 (2021).","journal-title":"JMIR Form. Res."},{"key":"1096_CR48","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.669811","volume":"12","author":"I Voigt","year":"2021","unstructured":"Voigt, I. et al. Digital twins for multiple sclerosis. Front. Immunol. 12, 669811 (2021).","journal-title":"Front. Immunol."},{"key":"1096_CR49","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooac072","volume":"5","author":"N Wickramasinghe","year":"2022","unstructured":"Wickramasinghe, N. et al. Digital twins to enable better precision and personalized dementia care. JAMIA Open. 5, ooac072 (2022).","journal-title":"JAMIA Open."},{"key":"1096_CR50","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1038\/s41746-022-00694-7","volume":"5","author":"KP Venkatesh","year":"2022","unstructured":"Venkatesh, K. P., Raza, M. M. & Kvedar, J. C. Health digital twins as tools for precision medicine: considerations for computation, implementation, and regulation. npj Digit. Med. 5, 150 (2022).","journal-title":"npj Digit. Med."},{"key":"1096_CR51","doi-asserted-by":"publisher","DOI":"10.1111\/cts.13897","volume":"17","author":"AA Vidovszky","year":"2024","unstructured":"Vidovszky, A. A. et al. Increasing acceptance of AI-generated digital twins through clinical trial applications. Clin. Transl. Sci. 17, e13897 (2024).","journal-title":"Clin. Transl. Sci."},{"key":"1096_CR52","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1177\/1553350620975896","volume":"28","author":"H Ahmed","year":"2021","unstructured":"Ahmed, H. & Devoto, L. The potential of a digital twin in surgery. Surg. Innov. 28, 509\u2013510 (2021).","journal-title":"Surg. Innov."},{"key":"1096_CR53","doi-asserted-by":"publisher","DOI":"10.1038\/s41585-024-00941-4","author":"E Checcucci","year":"2024","unstructured":"Checcucci, E. et al. Metaverse in surgery \u2014 origins and future potential. Nat. Rev. Urol. https:\/\/doi.org\/10.1038\/s41585-024-00941-4 (2024).","journal-title":"Nat. Rev. Urol."},{"key":"1096_CR54","doi-asserted-by":"publisher","DOI":"10.1002\/bco2.453","volume":"6","author":"D Amparore","year":"2025","unstructured":"Amparore, D. et al. 3D anatomical digital twins: new generation virtual models to navigate robotic partial nephrectomy. BJUI Compass 6, e453\u00a0(2025).","journal-title":"BJUI Compass"},{"key":"1096_CR55","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s41746-024-01413-0","volume":"8","author":"L Asciak","year":"2025","unstructured":"Asciak, L. et al. Digital twin assisted surgery, concept, opportunities, and challenges. npj Digit. Med. 8, 32 (2025).","journal-title":"npj Digit. Med."},{"key":"1096_CR56","doi-asserted-by":"publisher","first-page":"316","DOI":"10.3390\/fluids8120316","volume":"8","author":"J Cowley","year":"2023","unstructured":"Cowley, J., Luo, X., Stewart, G. D., Shu, W. & Kazakidi, A. A mathematical model of blood loss during renal resection. Fluids 8, 316 (2023).","journal-title":"Fluids"},{"key":"1096_CR57","doi-asserted-by":"publisher","unstructured":"Kim, J. W. et al. Surgical robot transformer (srt): imitation learning for surgical tasks. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2407.12998 (2024).","DOI":"10.48550\/arXiv.2407.12998"},{"key":"1096_CR58","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TVCG.2024.3456160","volume":"31","author":"A Wentzel","year":"2024","unstructured":"Wentzel, A. et al. DITTO: a visual digital twin for interventions and temporal treatment outcomes in head and neck cancer. IEEE Trans. Vis. Comput. Graph. 31, 65\u201375 (2024).","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"1096_CR59","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.1007784","volume":"4","author":"EA Stahlberg","year":"2022","unstructured":"Stahlberg, E. A. et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Front. Digit. Health 4, 1007784 (2022).","journal-title":"Front. Digit. Health"},{"key":"1096_CR60","doi-asserted-by":"publisher","unstructured":"Nitschke, A.-K. et al. Design for a digital twin in clinical patient care. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2505.01206 (2025).","DOI":"10.48550\/arXiv.2505.01206"},{"key":"1096_CR61","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1146\/annurev-bioeng-110915-114742","volume":"18","author":"M Viceconti","year":"2016","unstructured":"Viceconti, M. & Hunter, P. The virtual physiological human: ten years after. Annu. Rev. Biomed. Eng. 18, 103\u2013123 (2016).","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"1096_CR62","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1515\/cdbme-2021-2057","volume":"7","author":"J Gaebel","year":"2021","unstructured":"Gaebel, J. et al. The digital twin: modular model-based approach to personalized medicine. Curr. Dir Biomed. Eng. 7, 223\u2013226 (2021).","journal-title":"Curr. Dir Biomed. Eng."},{"key":"1096_CR63","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/jimaging9040081","volume":"9","author":"A Kebaili","year":"2023","unstructured":"Kebaili, A., Lapuyade-Lahorgue, J. & Ruan, S. Deep learning approaches for data augmentation in medical imaging: a review. J. Imaging 9, 81 (2023).","journal-title":"J. Imaging"},{"key":"1096_CR64","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","volume":"5","author":"RJ Chen","year":"2021","unstructured":"Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K. & Mahmood, F. Synthetic data in machine learning for medicine and healthcare. Nat. Biomed. Eng. 5, 493\u2013497 (2021).","journal-title":"Nat. Biomed. Eng."},{"key":"1096_CR65","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3389\/fgene.2018.00031","volume":"9","author":"K Bruynseels","year":"2018","unstructured":"Bruynseels, K., Santoni de Sio, F. & van den Hoven, J. Digital twins in health care: ethical implications of an emerging engineering paradigm. Front. Genet. 9, 31 (2018).","journal-title":"Front. Genet."},{"key":"1096_CR66","doi-asserted-by":"publisher","first-page":"3089","DOI":"10.1038\/s41388-023-02826-z","volume":"42","author":"I El Naqa","year":"2023","unstructured":"El Naqa, I. et al. Translation of AI into oncology clinical practice. Oncogene 42, 3089\u20133097 (2023).","journal-title":"Oncogene"},{"key":"1096_CR67","doi-asserted-by":"publisher","first-page":"715","DOI":"10.21037\/atm.2019.11.110","volume":"8","author":"D Lin","year":"2020","unstructured":"Lin, D. & Lin, H. Translating artificial intelligence into clinical practice. Ann. Transl. Med. 8, 715 (2020).","journal-title":"Ann. Transl. Med."},{"key":"1096_CR68","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1038\/s41746-023-00988-4","volume":"7","author":"MM Raza","year":"2024","unstructured":"Raza, M. M., Venkatesh, K. P. & Kvedar, J. C. Generative AI and large language models in health care: pathways to implementation. npj Digit. Med. 7, 62 (2024).","journal-title":"npj Digit. Med."},{"key":"1096_CR69","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1016\/j.cmet.2024.05.006","volume":"36","author":"E Zaunseder","year":"2024","unstructured":"Zaunseder, E. et al. Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases. Cell Metab. 36, 1882\u20131897.e7 (2024).","journal-title":"Cell Metab."},{"key":"1096_CR70","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N. et al. The future of digital health with federated learning. npj Digit. Med. 3, 119 (2020).","journal-title":"npj Digit. Med."},{"key":"1096_CR71","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/ejhg.2014.71","volume":"23","author":"J Kaye","year":"2015","unstructured":"Kaye, J. et al. Dynamic consent: a patient interface for twenty-first century research networks. Eur. J. Hum. Genet. 23, 141\u2013146 (2015).","journal-title":"Eur. J. Hum. Genet."},{"key":"1096_CR72","doi-asserted-by":"publisher","first-page":"866","DOI":"10.7326\/M18-1990","volume":"169","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G. & Chin, M. H. Ensuring fairness in machine learning to advance health equity. Ann. Intern. Med. 169, 866\u2013872 (2018).","journal-title":"Ann. Intern. Med."},{"key":"1096_CR73","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1038\/s43018-024-00891-1","volume":"6","author":"J Keyl","year":"2025","unstructured":"Keyl, J. et al. Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence. Nat. Cancer 6, 307\u2013322 (2025).","journal-title":"Nat. Cancer"},{"key":"1096_CR74","doi-asserted-by":"publisher","first-page":"4808","DOI":"10.1093\/eurheartj\/ehae619","volume":"45","author":"PM Thangaraj","year":"2024","unstructured":"Thangaraj, P. M., Benson, S. H., Oikonomou, E. K., Asselbergs, F. W. & Khera, R. cardiovascular care with digital twin technology in the era of generative artificial intelligence. Eur. Heart J. 45, 4808\u20134821 (2024).","journal-title":"Eur. Heart J."},{"key":"1096_CR75","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/s42256-024-00976-7","volume":"7","author":"M Steyvers","year":"2025","unstructured":"Steyvers, M. et al. What large language models know and what people think they know. Nat. Mach. Intell. 7, 221\u2013231 (2025).","journal-title":"Nat. Mach. Intell."},{"key":"1096_CR76","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.1038\/s41591-024-03097-1","volume":"30","author":"P Hager","year":"2024","unstructured":"Hager, P. et al. Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat. Med. 30, 2613\u20132622 (2024).","journal-title":"Nat. Med."},{"key":"1096_CR77","doi-asserted-by":"publisher","DOI":"10.1126\/science.adv4414","volume":"387","author":"EJ Topol","year":"2025","unstructured":"Topol, E. J. Learning the language of life with AI. Science 387, eadv4414 (2025).","journal-title":"Science"},{"key":"1096_CR78","doi-asserted-by":"publisher","first-page":"7045","DOI":"10.1016\/j.cell.2024.11.015","volume":"187","author":"C Bunne","year":"2024","unstructured":"Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045\u20137063 (2024).","journal-title":"Cell"},{"key":"1096_CR79","doi-asserted-by":"publisher","unstructured":"Kim, Y. et al. Medical hallucination in foundation models and their impact on healthcare. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2503.05777 (2025).","DOI":"10.48550\/arXiv.2503.05777"},{"key":"1096_CR80","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-99608-2","volume":"15","author":"R Cremese","year":"2025","unstructured":"Cremese, R. et al. Virtual reality-augmented differentiable simulations for digital twin applications in surgical planning. Sci. Rep. 15, 24377 (2025).","journal-title":"Sci. Rep."},{"key":"1096_CR81","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1093\/jamia\/ocv189","volume":"23","author":"JC Mandel","year":"2016","unstructured":"Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S. & Ramoni, R. B. SMART on FHIR: a standards-based, interoperable apps platform for electronic health records. J. Am. Med. Inf. Assoc. 23, 899\u2013908 (2016).","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"1096_CR82","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","volume":"25","author":"J Wiens","year":"2019","unstructured":"Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337\u20131340 (2019).","journal-title":"Nat. Med."},{"key":"1096_CR83","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/s41746-025-01544-y","volume":"8","author":"GE Weissman","year":"2025","unstructured":"Weissman, G. E., Mankowitz, T. & Kanter, G. P. Unregulated large language models produce medical device-like output. npj Digit. Med. 8, 148 (2025).","journal-title":"npj Digit. Med."},{"key":"1096_CR84","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med."},{"key":"1096_CR85","unstructured":"Bussone, A., Stumpf, S. & O\u2019Sullivan, D. in 2015 International Conference on Healthcare Informatics. 160\u2013169 (IEEE)."},{"key":"1096_CR86","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A. B. et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. fusion. 58, 82\u2013115 (2020).","journal-title":"Inf. fusion."},{"key":"1096_CR87","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206\u2013215 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"1096_CR88","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.juro.2017.03.130","volume":"198","author":"FA Distler","year":"2017","unstructured":"Distler, F. A. et al. The value of PSA density in combination with PI-RADS for the accuracy of prostate cancer prediction. J. Urol. 198, 575\u2013582 (2017).","journal-title":"J. Urol."},{"key":"1096_CR89","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1016\/j.eururo.2017.03.039","volume":"72","author":"JP Radtke","year":"2017","unstructured":"Radtke, J. P. et al. Combined clinical parameters and multiparametric magnetic resonance imaging for advanced risk modeling of prostate cancer-patient-tailored risk stratification can reduce unnecessary biopsies. Eur. Urol. 72, 888\u2013896 (2017).","journal-title":"Eur. Urol."},{"key":"1096_CR90","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.euo.2020.08.001","volume":"5","author":"A Lantz","year":"2022","unstructured":"Lantz, A. et al. Expanding active surveillance inclusion criteria: a novel nomogram including preoperative clinical parameters and magnetic resonance imaging findings. Eur. Urol. Oncol. 5, 187\u2013194 (2022).","journal-title":"Eur. Urol. Oncol."},{"key":"1096_CR91","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.eururo.2019.09.005","volume":"77","author":"G Gandaglia","year":"2020","unstructured":"Gandaglia, G. et al. The key combined value of multiparametric magnetic resonance imaging, and magnetic resonance imaging-targeted and concomitant systematic biopsies for the prediction of adverse pathological features in prostate cancer patients undergoing radical prostatectomy. Eur. Urol. 77, 733\u2013741 (2020).","journal-title":"Eur. Urol."},{"key":"1096_CR92","unstructured":"The European Parliament and the Council of the European Union. Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act) https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX:32024R1689 (2024)."},{"key":"1096_CR93","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1001\/jama.2024.21451","volume":"333","author":"HJ Warraich","year":"2025","unstructured":"Warraich, H. J., Tazbaz, T. & Califf, R. M. FDA perspective on the regulation of artificial intelligence in health care and biomedicine. JAMA 333, 241\u2013247 (2025).","journal-title":"JAMA"},{"key":"1096_CR94","unstructured":"HMA\u2013EMA Big Data Steering Group. Artificial intelligence workplan to guide use of AI in medicines regulation. EMA https:\/\/www.ema.europa.eu\/en\/news\/artificial-intelligence-workplan-guide-use-ai-medicines-regulation (2023)."},{"key":"1096_CR95","unstructured":"European Medicines Agency. Qualification opinion for prognostic covariate adjustment (PROCOVA\u2122). EMA https:\/\/www.ema.europa.eu\/en\/documents\/regulatory-procedural-guideline\/qualification-opinion-prognostic-covariate-adjustment-procovatm_en.pdf (2022)."},{"key":"1096_CR96","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1109\/MNET.118.2200071","volume":"37","author":"SD Okegbile","year":"2022","unstructured":"Okegbile, S. D., Cai, J., Niyato, D. & Yi, C. Human digital twin for personalized healthcare: vision, architecture and future directions. IEEE Netw. 37, 262\u2013269 (2022).","journal-title":"IEEE Netw."},{"key":"1096_CR97","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1038\/s41591-025-03545-6","volume":"31","author":"P Quattroni","year":"2025","unstructured":"Quattroni, P. et al. Five ways to enhance the diversity and quality of health data. Nat. Med. 31, 1747\u20131750 (2025).","journal-title":"Nat. Med."},{"key":"1096_CR98","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-023-00351-6","volume":"7","author":"ATM Cheung","year":"2023","unstructured":"Cheung, A. T. M. et al. Racial and ethnic disparities in a real-world precision oncology data registry. npj Precis. Oncol. 7, 7 (2023).","journal-title":"npj Precis. Oncol."},{"key":"1096_CR99","doi-asserted-by":"publisher","DOI":"10.1038\/s41568-025-00850-7","author":"US Asghar","year":"2025","unstructured":"Asghar, U. S. & Chung, C. Application of digital twins for personalized oncology. Nat. Rev. Cancer https:\/\/doi.org\/10.1038\/s41568-025-00850-7 (2025).","journal-title":"Nat. Rev. Cancer"},{"key":"1096_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.108589","volume":"27","author":"TE Yankeelov","year":"2024","unstructured":"Yankeelov, T. E. et al. Designing clinical trials for patients who are not average. iScience 27, 108589 (2024).","journal-title":"iScience"},{"key":"1096_CR101","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-91430-0","volume":"15","author":"KF Hossain","year":"2025","unstructured":"Hossain, K. F., Kamran, S. A., Ong, J. & Tavakkoli, A. Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation. Sci. Rep. 15, 15948 (2025).","journal-title":"Sci. Rep."},{"key":"1096_CR102","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.diii.2024.06.002","volume":"105","author":"D Ueda","year":"2024","unstructured":"Ueda, D. et al. Climate change and artificial intelligence in healthcare: review and recommendations towards a sustainable future. Diagn. Interv. Imaging 105, 453\u2013459 (2024).","journal-title":"Diagn. Interv. Imaging"}],"container-title":["Nature Reviews Urology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41585-025-01096-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41585-025-01096-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41585-025-01096-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T01:02:15Z","timestamp":1767747735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41585-025-01096-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":102,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["1096"],"URL":"https:\/\/doi.org\/10.1038\/s41585-025-01096-6","relation":{},"ISSN":["1759-4812","1759-4820"],"issn-type":[{"value":"1759-4812","type":"print"},{"value":"1759-4820","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"18 September 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.S. is an employee of Siemens Healthineers AG. M.B. is an\u00a0employee of SAP SE. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}