{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T03:23:17Z","timestamp":1787282597872,"version":"build-2736575974"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T00:00:00Z","timestamp":1781308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T00:00:00Z","timestamp":1787270400000},"content-version":"vor","delay-in-days":69,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03619-6","type":"journal-article","created":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T09:43:11Z","timestamp":1781343791000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data"],"prefix":"10.1186","volume":"26","author":[{"given":"Sergio","family":"Consoli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Katsimpokis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonello","family":"Meloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Reforgiato Recupero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthijs","family":"Sloep","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,13]]},"reference":[{"key":"3619_CR1","doi-asserted-by":"publisher","unstructured":"Bhuyan SS, Sateesh V, Mukul N, Galvankar A, Mahmood A, Nauman M, et al. Generative artificial intelligence use in healthcare: opportunities for clinical excellence and administrative efficiency. J Med Syst. 2025;49(1). https:\/\/doi.org\/10.1007\/s10916-024-02136-1.","DOI":"10.1007\/s10916-024-02136-1"},{"key":"3619_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.21037\/jmai-24-294","volume":"8","author":"W Ullah","year":"2025","unstructured":"Ullah W, Ali Q. Role of artificial intelligence in healthcare settings: a systematic review. J Med Artif Intell. 2025;8:24. https:\/\/doi.org\/10.21037\/jmai-24-294.","journal-title":"J Med Artif Intell"},{"key":"3619_CR3","doi-asserted-by":"publisher","first-page":"105474","DOI":"10.1016\/j.ijmedinf.2024.105474","volume":"188","author":"K Moulaei","year":"2024","unstructured":"Moulaei K, Yadegari A, Baharestani M, Farzanbakhsh S, Sabet B, Reza Afrash M. Generative artificial intelligence in healthcare: a scoping review on benefits, challenges and applications. Int J Multiling Med Inf. 2024;188:105474. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105474.","journal-title":"Int J Multiling Med Inf"},{"key":"3619_CR4","doi-asserted-by":"publisher","unstructured":"Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1). https:\/\/doi.org\/10.1186\/s12909-023-04698-z.","DOI":"10.1186\/s12909-023-04698-z"},{"issue":"8","key":"3619_CR5","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan, Ting DSW, et al. Large language models in medicine. Nat Med. 2023;29(8):1930\u201340. https:\/\/doi.org\/10.1038\/s41591-023-02448-8.","journal-title":"Nat Med"},{"key":"3619_CR6","doi-asserted-by":"publisher","unstructured":"Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. npj Precis Onc. 2024;8(1). https:\/\/doi.org\/10.1038\/s41698-024-00575-0.","DOI":"10.1038\/s41698-024-00575-0"},{"key":"3619_CR7","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.semcancer.2023.04.009","volume":"93","author":"Z Wang","year":"2023","unstructured":"Wang Z, Liu Y, Niu X. Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology. Semin Cancer Biol. 2023;93:83\u201396. https:\/\/doi.org\/10.1016\/j.semcancer.2023.04.009.","journal-title":"Semin Cancer Biol"},{"issue":"5","key":"3619_CR8","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1158\/2159-8290.CD-23-1199","volume":"14","author":"W Lotter","year":"2024","unstructured":"Lotter W, Hassett MJ, Schultz N, Kehl KL, Van Allen EM, Cerami E. Artificial intelligence in oncology: current landscape, challenges, and future directions. Cancer Discov. 2024;14(5):711\u201326. https:\/\/doi.org\/10.1158\/2159-8290.CD-23-1199.","journal-title":"Cancer Discov"},{"issue":"12","key":"3619_CR9","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.1002\/cncr.35307","volume":"130","author":"L Kolla","year":"2024","unstructured":"Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer. 2024;130(12):2101\u201307. https:\/\/doi.org\/10.1002\/cncr.35307.","journal-title":"Cancer"},{"key":"3619_CR10","doi-asserted-by":"publisher","unstructured":"Fountzilas E, Pearce T, Baysal MA, Chakraborty A, Tsimberidou AM. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. NPJ Digit Med. 2025;8(1). https:\/\/doi.org\/10.1038\/s41746-025-01471-y.","DOI":"10.1038\/s41746-025-01471-y"},{"issue":"1","key":"3619_CR11","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s10143-025-03419-y","volume":"48","author":"I Mohammadzadeh","year":"2025","unstructured":"Mohammadzadeh I, Hajikarimloo B, Niroomand B, Eini P, Ghanbarnia R, Habibi MA, et al. Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants. Neurosurg Rev. 2025;48(1):240. https:\/\/doi.org\/10.1007\/s10143-025-03419-y.","journal-title":"Neurosurg Rev"},{"key":"3619_CR12","doi-asserted-by":"publisher","unstructured":"Feng Y, Wang Z, Cui R, Xiao M, Gao H, Bai H, et al. Clinical analysis and artificial intelligence survival prediction of serous ovarian cancer based on preoperative circulating leukocytes. J Ovarian Res. 2022;15(1). https:\/\/doi.org\/10.1186\/s13048-022-00994-2.","DOI":"10.1186\/s13048-022-00994-2"},{"key":"3619_CR13","doi-asserted-by":"publisher","first-page":"109391","DOI":"10.1016\/j.compbiomed.2024.109391","volume":"184","author":"A Uwimana","year":"2025","unstructured":"Uwimana A, Gnecco G, Riccaboni M. Artificial intelligence for breast cancer detection and its health technology assessment: a scoping review. Comput Biol Med. 2025;184:109391. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.109391.","journal-title":"Comput Biol Med"},{"key":"3619_CR14","doi-asserted-by":"publisher","unstructured":"Javanmard Z, Zarean Shahraki S, Safari K, Omidi A, Raoufi S, Rajabi M, et al. Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis. Front Oncol. 2025;14. https:\/\/doi.org\/10.3389\/fonc.2024.1420328.","DOI":"10.3389\/fonc.2024.1420328"},{"issue":"5","key":"3619_CR15","doi-asserted-by":"publisher","first-page":"405","DOI":"10.4048\/jbc.2023.26.e45","volume":"26","author":"JS Ahn","year":"2023","unstructured":"Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, et al. Artificial Intelligence in breast cancer diagnosis and personalized medicine. J Breast Cancer. 2023;26(5):405\u201335. https:\/\/doi.org\/10.4048\/jbc.2023.26.e45.","journal-title":"J Breast Cancer"},{"key":"3619_CR16","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;5999\u20136009. https:\/\/doi.org\/10.5555\/3295222.3295349.","DOI":"10.5555\/3295222.3295349"},{"issue":"159","key":"3619_CR17","first-page":"1877","volume":"6","author":"TB Brown","year":"2020","unstructured":"Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;6(159):1877\u2013901. https:\/\/dl.acm.org\/doi\/abs\/10.5555\/3495724.3495883.","journal-title":"Adv. Neural Inf. Process. Syst"},{"key":"3619_CR18","doi-asserted-by":"publisher","first-page":"115160","DOI":"10.1016\/j.jbusres.2024.115160","volume":"189","author":"A Kumar","year":"2025","unstructured":"Kumar A, Shankar A, Hollebeek LD, Behl A, Lim WM. Generative artificial intelligence (GenAI) revolution: a deep dive into GenAI adoption. J Educ Chang Bus Res. 2025;189:115160. https:\/\/doi.org\/10.1016\/j.jbusres.2024.115160.","journal-title":"J Educ Chang Bus Res"},{"issue":"4","key":"3619_CR19","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1001\/jama.2024.21700","volume":"333","author":"S Bedi","year":"2025","unstructured":"Bedi S, Liu Y, Orr-Ewing L, Dash D, Koyejo S, Callahan A, et al. Testing and evaluation of health care applications of large language models: a systematic review. JAMA. 2025;333(4):319. https:\/\/doi.org\/10.1001\/jama.2024.21700.","journal-title":"JAMA"},{"key":"3619_CR20","doi-asserted-by":"publisher","first-page":"e59069","DOI":"10.2196\/59069","volume":"27","author":"K Zhang","year":"2025","unstructured":"Zhang K, Meng X, Yan X, Ji J, Liu J, Xu H, et al. Revolutionizing health care: the transformative impact of large language models in medicine. J Med Internet Res. 2025;27:e59069. https:\/\/doi.org\/10.2196\/59069.","journal-title":"J Med Internet Res"},{"key":"3619_CR21","unstructured":"Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst. 2014;3104\u201312. https:\/\/dl.acm.org\/doi\/10.5555\/2969033.2969173."},{"key":"3619_CR22","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.1016\/j.csbj.2024.07.005","volume":"23","author":"VC Pezoulas","year":"2024","unstructured":"Pezoulas VC, Zaridis DI, Mylona E, Androutsos C, Apostolidis K, Tachos NS, et al. Synthetic data generation methods in healthcare: a review on open-source tools and methods. Comput Struct Biotechnol J. 2024;23:2892\u2013910. https:\/\/doi.org\/10.1016\/j.csbj.2024.07.005.","journal-title":"Comput Struct Biotechnol J"},{"key":"3619_CR23","doi-asserted-by":"publisher","unstructured":"Miletic M, Sariyar M. Synthetic data generation methods for longitudinal and time series health data: a systematic review. BMC Med Inf Decis Mak. 2025;26(1):early access. https:\/\/doi.org\/10.1186\/s12911-025-03326-8.","DOI":"10.1186\/s12911-025-03326-8"},{"key":"3619_CR24","doi-asserted-by":"publisher","unstructured":"Lee SW. Kaplan-meier and Cox proportional hazards regression in survival analysis: statistical standard and guideline of Life Cycle Committee. Life Cycle. 2023;3:e8. https:\/\/doi.org\/10.54724\/lc.2023.e8.","DOI":"10.54724\/lc.2023.e8"},{"issue":"6","key":"3619_CR25","doi-asserted-by":"publisher","first-page":"bbae508","DOI":"10.1093\/bib\/bbae508","volume":"25","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Muller S. Robust variable selection methods with Cox model\u2014a selective practical benchmark study. Briefings Bioinf. 2024, 10;25(6):bbae508. https:\/\/doi.org\/10.1093\/bib\/bbae508.","journal-title":"Briefings Bioinf"},{"issue":"4","key":"3619_CR26","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.gpb.2016.03.006","volume":"14","author":"FM Ojeda","year":"2016","unstructured":"Ojeda FM, M\u00fcller C, B\u00f6rnigen D, Tr\u00e9gou\u00ebt DA, Schillert A, Heinig M, et al. Comparison of Cox model methods in a low-dimensional setting with few events. Genomics Proteomics Bioinf. 2016, Aug;14(4):235\u201343. https:\/\/doi.org\/10.1016\/j.gpb.2016.03.006.","journal-title":"Genomics Proteomics Bioinf"},{"issue":"1","key":"3619_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1186\/s12911-024-02525-z","volume":"24","author":"N Asghar","year":"2024","unstructured":"Asghar N, Khalil U, Ahmad B, Alshanbari HM, Hamraz M, Ahmad B, et al. Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network. BMC Med Inf Decis Mak. 2024;24(1):120. https:\/\/doi.org\/10.1186\/s12911-024-02525-z.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3619_CR28","doi-asserted-by":"publisher","DOI":"10.1201\/b18041","volume-title":"Modelling survival data in medical research","author":"D Collett","year":"2014","unstructured":"Collett D. Modelling survival data in medical research. 3rd. New York, US: Chapman and Hall\/CRC; 2014. Available from: https:\/\/doi.org\/10.1201\/b18041.","edition":"3rd"},{"issue":"6","key":"3619_CR29","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1093\/aje\/kwae361","volume":"194","author":"MJ Stensrud","year":"2025","unstructured":"Stensrud MJ, Hern\u00e1n MA. Why use methods that require proportional hazards? Am J Epidemiol. 2025, 01;194(6):1504\u201306. https:\/\/doi.org\/10.1093\/aje\/kwae361.","journal-title":"Am J Epidemiol"},{"issue":"3","key":"3619_CR30","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1093\/biomet\/asm037","volume":"94","author":"HH Zhang","year":"2007","unstructured":"Zhang HH, Lu W. Adaptive Lasso for Cox\u2019s proportional hazards model. Biometrika. 2007;94(3):691\u2013703. https:\/\/doi.org\/10.1093\/biomet\/asm037.","journal-title":"Biometrika"},{"key":"3619_CR31","doi-asserted-by":"publisher","unstructured":"Pickett KL, Suresh K, Campbell KR, Davis S, Juarez-Colunga E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC Med Res Methodol. 2021;21(1). https:\/\/doi.org\/10.1186\/s12874-021-01375-x.","DOI":"10.1186\/s12874-021-01375-x"},{"key":"3619_CR32","doi-asserted-by":"publisher","unstructured":"Qiu X, Gao J, Yang J, Hu J, Hu W, Kong L, et al. A comparison study of machine learning (random survival forest) and classic statistic (Cox proportional hazards) for predicting progression in high-grade glioma after Proton and carbon ion radiotherapy. Front Oncol. 2020;10. https:\/\/doi.org\/10.3389\/fonc.2020.551420.","DOI":"10.3389\/fonc.2020.551420"},{"issue":"3","key":"3619_CR33","doi-asserted-by":"publisher","first-page":"378","DOI":"10.18502\/jbe.v9i3.15450","volume":"9","author":"N Taghavi Razavizadeh","year":"2023","unstructured":"Taghavi Razavizadeh N, Salari M, Jafari M, Sabaghian E, Ghavami V. Comparison of two methods, gradient boosting and extreme gradient boosting to predict survival in covid-19 data. J Retailing Biostat Epidemiol. 2023;9(3):378\u201387. https:\/\/doi.org\/10.18502\/jbe.v9i3.15450.","journal-title":"J Retailing Biostat Epidemiol"},{"issue":"1","key":"3619_CR34","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/s12911-025-03205-2","volume":"25","author":"N Shabani","year":"2025","unstructured":"Shabani N, Yaseri M, Alimi R, Nazemian F, Zeraati H. Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data. BMC Med Inf Decis Mak. 2025;25(1):368. https:\/\/doi.org\/10.1186\/s12911-025-03205-2.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"1","key":"3619_CR35","doi-asserted-by":"publisher","first-page":"6968","DOI":"10.1038\/s41598-021-86327-7","volume":"11","author":"A Moncada-Torres","year":"2021","unstructured":"Moncada-Torres A, van Maaren MC, Hendriks MP, Siesling S, Geleijnse G. Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival. Sci Rep. 2021, 3;11(1):6968. https:\/\/doi.org\/10.1038\/s41598-021-86327-7.","journal-title":"Sci Rep"},{"issue":"1","key":"3619_CR36","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1186\/s12874-020-01153-1","volume":"20","author":"G Kantidakis","year":"2020","unstructured":"Kantidakis G, Putter H, Lancia C, Boer J, Braat AE, Fiocco M. Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques. BMC Med Res Methodol. 2020, 11;20(1):277. https:\/\/doi.org\/10.1186\/s12874-020-01153-1.","journal-title":"BMC Med Res Methodol"},{"key":"3619_CR37","doi-asserted-by":"publisher","unstructured":"Katsimpokis D, van Odenhoven AEC, van Erp MAJM, Wenzel HHB, van der Aa MA, van Swieten MMH, et al. Ovarian cancer recurrence prediction: comparing confirmatory to real world predictors with machine learning. medRxiv. 2025. https:\/\/doi.org\/10.1101\/2025.03.04.25321571.","DOI":"10.1101\/2025.03.04.25321571"},{"issue":"4","key":"3619_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0318167","volume":"20","author":"TG Baidoo","year":"2025","unstructured":"Baidoo TG, Rodrigo H. Data-driven survival modeling for breast cancer prognostics: a comparative study with machine learning and traditional survival modeling methods. PLoS One. 2025;20(4):1\u201318. https:\/\/doi.org\/10.1371\/journal.pone.0318167.","journal-title":"PLoS One"},{"issue":"1","key":"3619_CR39","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18(1):24. https:\/\/doi.org\/10.1186\/s12874-018-0482-1.","journal-title":"BMC Med Res Methodol"},{"key":"3619_CR40","doi-asserted-by":"publisher","unstructured":"Lee C, Zame W, Yoon J, van der Schaar M. DeepHit: a deep learning approach to survival analysis with competing risks. Proc AAAI Conf Artif Intel. 2018;32(1). https:\/\/doi.org\/10.1609\/aaai.v32i1.11842.","DOI":"10.1609\/aaai.v32i1.11842"},{"key":"3619_CR41","doi-asserted-by":"publisher","first-page":"105695","DOI":"10.1016\/j.ebiom.2025.105695","volume":"115","author":"S Liang","year":"2025","unstructured":"Liang S, Zhang J, Liu X, Huang Y, Shao J, Liu X, et al. The potential of large language models to advance precision oncology. eBiomedicine. 2025;115:105695. https:\/\/doi.org\/10.1016\/j.ebiom.2025.105695.","journal-title":"eBiomedicine"},{"issue":"1","key":"3619_CR42","doi-asserted-by":"publisher","first-page":"e000759","DOI":"10.1136\/bmjonc-2025-000759","volume":"4","author":"D Chen","year":"2025","unstructured":"Chen D, Parsa R, Swanson K, Nunez JJ, Critch A, Bitterman DS, et al. Large language models in oncology: a review. BMJ Oncol. 2025;4(1):e000759. https:\/\/doi.org\/10.1136\/bmjonc-2025-000759.","journal-title":"BMJ Oncol"},{"key":"3619_CR43","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.ymeth.2025.04.001","volume":"239","author":"M Ghorbian","year":"2025","unstructured":"Ghorbian M, Ghobaei-Arani M, Ghorbian S. Transforming breast cancer diagnosis and treatment with large language models: a comprehensive survey. Methods. 2025;239:85\u2013110. https:\/\/doi.org\/10.1016\/j.ymeth.2025.04.001.","journal-title":"Methods"},{"issue":"7995","key":"3619_CR44","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1038\/s41586-023-06924-6","volume":"625","author":"B Romera-Paredes","year":"2024","unstructured":"Romera-Paredes B, Barekatain M, Novikov A, Balog M, Kumar MP, Dupont E, et al. Mathematical discoveries from program search with large language models. Nature. 2024;625(7995):468\u201375. https:\/\/doi.org\/10.1038\/s41586-023-06924-6.","journal-title":"Nature"},{"issue":"8055","key":"3619_CR45","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1038\/s41586-025-08661-4","volume":"639","author":"M Yuksekgonul","year":"2025","unstructured":"Yuksekgonul M, Bianchi F, Boen J, Liu S, Lu P, Huang Z, et al. Optimizing generative AI by backpropagating language model feedback. Nature. 2025;639(8055):609\u201316. https:\/\/doi.org\/10.1038\/s41586-025-08661-4.","journal-title":"Nature"},{"key":"3619_CR46","unstructured":"Vacareanu R, Negru VA, Suciu V, Surdeanu M. From words to numbers: your large language model is secretly a capable regressor when given In-context examples. arXiv. 2024. p. 2404.07544. Available from: https:\/\/arxiv.org\/abs\/2404.07544."},{"key":"3619_CR47","unstructured":"Dong Q, Li L, Dai D, Zheng C, Ma J, Li R, et al. In: Al-Onaizan Y, Bansal M, Chen YN, editors. A survey on In-context learning. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida, USA: Association for Computational Linguistics; 2024. p. 1107\u201328. Available from: https:\/\/aclanthology.org\/2024.emnlp-main.64\/."},{"key":"3619_CR48","doi-asserted-by":"publisher","unstructured":"Wang L, Chen X, Deng X, Wen H, You M, Liu W, et al. Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs. NPJ Digit Med. 2024;7(1). https:\/\/doi.org\/10.1038\/s41746-024-01029-4.","DOI":"10.1038\/s41746-024-01029-4"},{"key":"3619_CR49","doi-asserted-by":"publisher","unstructured":"Iannantuono GM, Bracken-Clarke D, Floudas CS, Roselli M, Gulley JL, Karzai F. Applications of large language models in cancer care: current evidence and future perspectives. Front Oncol. 2023;13. https:\/\/doi.org\/10.3389\/fonc.2023.1268915.","DOI":"10.3389\/fonc.2023.1268915"},{"key":"3619_CR50","doi-asserted-by":"publisher","first-page":"115274","DOI":"10.1016\/j.ejca.2025.115274","volume":"218","author":"BD Menz","year":"2025","unstructured":"Menz BD, Modi ND, Abuhelwa AY, Ruanglertboon W, Vitry A, Gao Y, et al. Generative AI chatbots for reliable cancer information: evaluating web-search, multilingual, and reference capabilities of emerging large language models. Eur J Criminol Cancer. 2025;218:115274. https:\/\/doi.org\/10.1016\/j.ejca.2025.115274.","journal-title":"Eur J Criminol Cancer"},{"key":"3619_CR51","doi-asserted-by":"publisher","first-page":"107327482311979","DOI":"10.1177\/10732748231197915","volume":"30","author":"A Laios","year":"2023","unstructured":"Laios A, Theophilou G, Jong DD, Kalampokis E. The future of AI in ovarian cancer research: The large language models perspective. Cancer Control: J Moffitt Cancer Cent. 2023;30:10732748231197915. https:\/\/doi.org\/10.1177\/10732748231197915.","journal-title":"Cancer Control: J Moffitt Cancer Cent"},{"issue":"2","key":"3619_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pdig.0000735","volume":"4","author":"D Pau","year":"2025","unstructured":"Pau D, Bachot C, Monteil C, Vinet L, Boucher M, Sella N, et al. Comparison of anonymization techniques regarding statistical reproducibility. PLoS Digit Health. 2025;4(2):1\u201319. https:\/\/doi.org\/10.1371\/journal.pdig.0000735.","journal-title":"PLoS Digit Health"},{"issue":"1","key":"3619_CR53","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1186\/s12911-021-01602-x","volume":"21","author":"FN Wirth","year":"2021","unstructured":"Wirth FN, Meurers T, Johns M, Prasser F. Privacy-preserving data sharing infrastructures for medical research: systematization and comparison. BMC Med Inf Decis Mak. 2021;21(1):242. https:\/\/doi.org\/10.1186\/s12911-021-01602-x.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3619_CR54","doi-asserted-by":"publisher","first-page":"108288","DOI":"10.1016\/j.compbiomed.2024.108288","volume":"172","author":"C Rollo","year":"2024","unstructured":"Rollo C, Pancotti C, Birolo G, Rossi I, Sanavia T, Fariselli P. SYNDSURV: a simple framework for survival analysis with data distributed across multiple institutions. Comput Biol Med. 2024;172:108288. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108288.","journal-title":"Comput Biol Med"},{"issue":"11","key":"3619_CR55","doi-asserted-by":"publisher","first-page":"e23139","DOI":"10.2196\/23139","volume":"22","author":"K El Emam","year":"2020","unstructured":"El Emam K, Mosquera L, Bass J. Evaluating identity disclosure risk in fully synthetic health data: model development and validation. J Med Internet Res. 2020, Nov;22(11):e23139. https:\/\/doi.org\/10.2196\/23139.","journal-title":"J Med Internet Res"},{"issue":"1","key":"3619_CR56","doi-asserted-by":"publisher","first-page":"25676","DOI":"10.1038\/s41598-024-72894-y","volume":"14","author":"Z Qian","year":"2024","unstructured":"Qian Z, Callender T, Cebere B, Janes SM, Navani N, van der Schaar M. Synthetic data for privacy-preserving clinical risk prediction. Sci Rep. 2024;14(1):25676. https:\/\/doi.org\/10.1038\/s41598-024-72894-y.","journal-title":"Sci Rep"},{"issue":"1","key":"3619_CR57","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s12874-020-00977-1","volume":"20","author":"A Goncalves","year":"2020","unstructured":"Goncalves A, Ray P, Soper B, Stevens J, Coyle L, Sales AP. Generation and evaluation of synthetic patient data. BMC Med Res Methodol. 2020;20(1):108. https:\/\/doi.org\/10.1186\/s12874-020-00977-1.","journal-title":"BMC Med Res Methodol"},{"key":"3619_CR58","doi-asserted-by":"publisher","unstructured":"Chatzichristos C, Katsimpokis D, Lai C, Van Santvliet L, Camarrone F, Knoors D, et al. Quality assessment of synthetic data in healthcare: a critical appraisal. JMIR Preprint. 2024; https:\/\/doi.org\/10.2196\/preprints.69390.","DOI":"10.2196\/preprints.69390"},{"key":"3619_CR59","doi-asserted-by":"publisher","first-page":"105763","DOI":"10.1016\/j.ijmedinf.2024.105763","volume":"195","author":"M Rujas","year":"2025","unstructured":"Rujas M, Mart\u00edn G\u00f3mez Del Moral Herranz R, Fico G, Merino-Barbancho B. Synthetic data generation in healthcare: a scoping review of reviews on domains, motivations, and future applications. Int J Multiling Med Inf. 2025;195:105763. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105763.","journal-title":"Int J Multiling Med Inf"},{"key":"3619_CR60","doi-asserted-by":"publisher","unstructured":"Smith A, Lambert PC, Rutherford MJ. Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility. BMC Med Res Methodol. 2022;22(1). https:\/\/doi.org\/10.1186\/s12874-022-01654-1.","DOI":"10.1186\/s12874-022-01654-1"},{"key":"3619_CR61","doi-asserted-by":"publisher","unstructured":"Tucker A, Wang Z, Rotalinti Y, Myles P. Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. NPJ Digit Med. 2020;3(1). https:\/\/doi.org\/10.1038\/S41746-020-00353-9.","DOI":"10.1038\/S41746-020-00353-9"},{"key":"3619_CR62","doi-asserted-by":"publisher","unstructured":"Goyal M, Mahmoud QH. A systematic review of synthetic data generation techniques using Generative AI. Electronics. 2024;13(17):10.3390\/electronics13173509. https:\/\/doi.org\/10.3390\/electronics13173509.","DOI":"10.3390\/electronics13173509"},{"issue":"4","key":"3619_CR63","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.semradonc.2023.06.004","volume":"33","author":"J Kang","year":"2023","unstructured":"Kang J, Chowdhry AK, Pugh SL, Park JH. Integrating artificial intelligence and machine learning into cancer clinical trials. Semin Radiat Oncol. 2023;33(4):386\u201394. Advances in Clinical Trial Design, Execution, and Implementation. https:\/\/doi.org\/10.1016\/j.semradonc.2023.06.004.","journal-title":"Semin Radiat Oncol"},{"key":"3619_CR64","doi-asserted-by":"publisher","first-page":"110560","DOI":"10.1016\/j.dib.2024.110560","volume":"55","author":"JM Au\u00f1\u00f3n","year":"2024","unstructured":"Au\u00f1\u00f3n JM, Hurtado-Ram\u00edrez D, Porras-D\u00edaz L, Irigoyen-Pe\u00f1a B, Rahmian S, Al-Khazraji Y, et al. Evaluation and utilisation of privacy enhancing technologies\u2014a data spaces perspective. Data Brief. 2024;55:110560. https:\/\/doi.org\/10.1016\/j.dib.2024.110560.","journal-title":"Data Brief"},{"issue":"11","key":"3619_CR65","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020;63(11):139\u201344. https:\/\/doi.org\/10.1145\/3422622.","journal-title":"Commun ACM"},{"issue":"2","key":"3619_CR66","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MCI.2022.3155327","volume":"17","author":"LV Jospin","year":"2022","unstructured":"Jospin LV, Laga H, Boussaid F, Buntine W, Bennamoun M. Hands-on Bayesian neural networks\u2014A tutorial for deep learning users. IEEE Comput Intell Mag. 2022;17(2):29\u201348. https:\/\/doi.org\/10.1109\/MCI.2022.3155327.","journal-title":"IEEE Comput Intell Mag"},{"key":"3619_CR67","doi-asserted-by":"publisher","first-page":"105413","DOI":"10.1016\/j.ijmedinf.2024.105413","volume":"185","author":"VB Vallevik","year":"2024","unstructured":"Vallevik VB, Babic A, Marshall SE, Elvatun S, Br\u00f8gger HMB, Alagaratnam S, et al. Can I trust my fake data \u2013 a comprehensive quality assessment framework for synthetic tabular data in healthcare. Int J Multiling Med Inf. 2024;185:105413. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105413.","journal-title":"Int J Multiling Med Inf"},{"issue":"4","key":"3619_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3134428","volume":"42","author":"J Zhang","year":"2017","unstructured":"Zhang J, Cormode G, Procopiuc CM, Srivastava D, Xiao X. PrivBayes: private data release via Bayesian networks. ACM Trans Database Syst. 2017;42(4):1\u201341. https:\/\/doi.org\/10.1145\/3134428.","journal-title":"ACM Trans Database Syst"},{"key":"3619_CR69","doi-asserted-by":"publisher","first-page":"22359","DOI":"10.1109\/ACCESS.2022.3151670","volume":"10","author":"AE Ouadrhiri","year":"2022","unstructured":"Ouadrhiri AE, Abdelhadi A. Differential privacy for deep and federated learning: a survey. IEEE Access. 2022;10:22359\u201380. https:\/\/doi.org\/10.1109\/ACCESS.2022.3151670.","journal-title":"IEEE Access"},{"issue":"1","key":"3619_CR70","doi-asserted-by":"publisher","first-page":"e0000581","DOI":"10.1371\/journal.pdig.0000581","volume":"4","author":"S Elvatun","year":"2025","unstructured":"Elvatun S, Knoors D, Brant S, Jonasson C, Nyg\u00e5rd JF. Synthetic data as external control arms in scarce single-arm clinical trials. PLoS Digit Health. 2025;4(1):e0000581. https:\/\/doi.org\/10.1371\/journal.pdig.0000581.","journal-title":"PLoS Digit Health"},{"key":"3619_CR71","doi-asserted-by":"publisher","unstructured":"Wang L, Wen A, Fu S, Ruan X, Huang M, Li R, et al. A scoping review of OMOP CDM adoption for cancer research using real world data. NPJ Digit Med. 2025;8(1). https:\/\/doi.org\/10.1038\/s41746-025-01581-7.","DOI":"10.1038\/s41746-025-01581-7"},{"issue":"1","key":"3619_CR72","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","volume":"28","author":"DJ Stekhoven","year":"2012","unstructured":"Stekhoven DJ, B\u00fchlmann P. MissForest\u2014non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28(1):112\u201318. https:\/\/doi.org\/10.1093\/bioinformatics\/btr597.","journal-title":"Bioinformatics"},{"issue":"10","key":"3619_CR73","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1002\/sim.4154","volume":"30","author":"H Uno","year":"2011","unstructured":"Uno H, Cai T, Pencina MJ, D\u2019Agostino RB, Wei LJ. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat Med. 2011;30(10):1105\u201317. https:\/\/doi.org\/10.1002\/sim.4154.","journal-title":"Stat Med"},{"issue":"2","key":"3619_CR74","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","volume":"34","author":"DR Cox","year":"1972","unstructured":"Cox DR. Regression models and life-tables. J R Stat Soc Ser B Stat Methodol. 1972;34(2):187\u2013202. https:\/\/doi.org\/10.1111\/j.2517-6161.1972.tb00899.x.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"1","key":"3619_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2013\/873595","volume":"2013","author":"Y Chen","year":"2013","unstructured":"Chen Y, Jia Z, Mercola D, Xie X. A gradient boosting algorithm for survival analysis via direct optimization of concordance Index. Comput Math Method M. 2013;2013(1):1\u20138. https:\/\/doi.org\/10.1155\/2013\/873595.","journal-title":"Comput Math Method M"},{"issue":"4","key":"3619_CR76","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btz682.","journal-title":"Bioinformatics"},{"key":"3619_CR77","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, et al. LLaMA: open and efficient foundation language models. arXiv. 2023. p. 2302.13971. Available from: https:\/\/arxiv.org\/abs\/2302.13971."},{"key":"3619_CR78","unstructured":"Grattafiori A, Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, et al. The LLaMA 3 herd of models. arXiv. 2024. p. 2407.21783. Available from: https:\/\/arxiv.org\/abs\/2407.21783."},{"key":"3619_CR79","unstructured":"Yang A, Yang B, Zhang B, Hui B, Zheng B, Yu B, et al. Qwen2.5 technical report. arXiv; 2025. p. 2412.15115. Available from: https:\/\/arxiv.org\/abs\/2412.15115."},{"issue":"1","key":"3619_CR80","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res. 2020;21(1):5485\u2013551.","journal-title":"J Mach Learn Res"},{"key":"3619_CR81","unstructured":"Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. In: Statistics for biology and health. New York, NY: Springer; 1997."},{"key":"3619_CR82","unstructured":"Karlsson RKA, Willbo M, Hussain Z, Krishnan RG, Sontag D, Johansson FD. Using time-series privileged information for provably efficient learning of prediction models. Proceedings of Machine Learning Research. 2022. p. 5459\u201384. vol. 151."},{"issue":"4","key":"3619_CR83","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1007\/s12350-014-9908-2","volume":"21","author":"B George","year":"2014","unstructured":"George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol. 2014;21(4):686\u201394. https:\/\/doi.org\/10.1007\/s12350-014-9908-2.","journal-title":"J Nucl Cardiol"},{"key":"3619_CR84","unstructured":"Jeanselme V, Yoon CH, Tom B, Barrett J. Neural fine-gray: monotonic neural networks for competing risks. Proc Mach Learn Res. 2023;209(379):\u2013392."},{"issue":"4","key":"3619_CR85","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1093\/JAMIA\/OCZ228","volume":"27","author":"Y Huang","year":"2021","unstructured":"Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inf Assoc. 2021;27(4):621\u201333. https:\/\/doi.org\/10.1093\/JAMIA\/OCZ228.","journal-title":"J Am Med Inf Assoc"},{"issue":"13","key":"3619_CR86","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1002\/sim.9717","volume":"42","author":"N Hartman","year":"2023","unstructured":"Hartman N, Kim S, He K, Kalbfleisch JD. Pitfalls of the concordance index for survival outcomes. Stat Med. 2023;42(13):2179\u201390. https:\/\/doi.org\/10.1002\/sim.9717.","journal-title":"Stat Med"},{"key":"3619_CR87","doi-asserted-by":"publisher","unstructured":"Nielsen F. On a generalization of the Jensen\u2013Shannon divergence and the Jensen\u2013Shannon centroid. Entropy. 2020;22(2):10.3390\/e22020221. https:\/\/doi.org\/10.3390\/e22020221.","DOI":"10.3390\/e22020221"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03619-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03619-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03619-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T02:28:22Z","timestamp":1787279302000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03619-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,13]]},"references-count":87,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3619"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03619-6","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,13]]},"assertion":[{"value":"21 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This retrospective study was conducted using (i) a fully synthetic breast cancer dataset generated by the Netherlands Comprehensive Cancer Organisation (IKNL), which contains no data relating to real or identifiable individuals, and (ii) analyses on pseudonymized and aggregated real-world population-based cancer registry data from the Netherlands Cancer Registry (NCR), performed within the secure research environment of IKNL. The use of fully synthetic data does not fall within the scope of human subjects research as defined by applicable national and European regulations. For analyses involving real-world registry data, all data were pseudonymized at source and processed in accordance with the governance framework of the Netherlands Cancer Registry. Data release by IKNL is subject to strict regulatory conditions and oversight by a Privacy Review Board and an independent scientific committee (NCR data application number: 25-00799). Ethical oversight for registry-based research using data from the Netherlands Cancer Registry is provided by the Netherlands Comprehensive Cancer Organisation, which acts as the responsible institutional authority for the Netherlands Cancer Registry. According to Dutch legislation, including the Medical Research Involving Human Subjects Act (WMO), this type of retrospective registry-based research does not require approval from a medical ethics review committee. The requirement for formal ethics approval was therefore waived.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research met all ethical guidelines, including adherence to the legal requirements of the study country.","order":2,"name":"Ethics","label":"Ethical standards","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent from individual patients was not required for this study. The synthetic dataset contains no real patient data. For the real-world registry component, data were collected as part of routine cancer registration under statutory mandate. The secondary use of these data for scientific research purposes is permitted under Dutch law and the General Data Protection Regulation (GDPR), subject to the governance and oversight procedures of the Netherlands Comprehensive Cancer Organisation (IKNL). The requirement for informed consent was therefore waived in accordance with national regulations and institutional governance procedures.","order":3,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All research involving human data was conducted in accordance with the ethical principles of the Declaration of Helsinki and its later amendments. The study relied exclusively on secondary use of registry data and synthetic data, involved no direct patient contact, and included no interventions. Appropriate safeguards for data protection, privacy, and confidentiality were applied throughout the study in compliance with applicable ethical and legal standards.","order":4,"name":"Ethics","label":"Declaration of Helsinki statement","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"321"}}