{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:18:07Z","timestamp":1791458287152,"version":"4.3.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"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":["Urologie"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00120-026-02831-6","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:23:27Z","timestamp":1778664207000},"page":"595-604","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["K\u00fcnstliche Intelligenz f\u00fcr Therapieentscheidungen in der urologischen Onkologie: aktuelle Evidenz und Herausforderungen","Artificial intelligence as decision support tool in urological oncology: current evidence and challenges"],"prefix":"10.1007","volume":"65","author":[{"given":"Lisa Maria","family":"Jost","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregor","family":"Duwe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Verena","family":"Kauth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"H\u00f6fner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Glienke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magdalena","family":"G\u00f6rtz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherif","family":"Mehralivand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hendrik","family":"Borgmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian Peter","family":"Struck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"2831_CR1","doi-asserted-by":"publisher","unstructured":"Maslej\u00a0N, Fattorini\u00a0L, Perrault\u00a0R et\u00a0al (2025) Artificial Intelligence Index Report 2025. arXivarXiv:2504.07139. https:\/\/doi.org\/10.48550\/arXiv.2504.07139","DOI":"10.48550\/arXiv.2504.07139"},{"issue":"3","key":"2831_CR2","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1111\/bju.15662","volume":"130","author":"A Stenzl","year":"2022","unstructured":"Stenzl\u00a0A, Sternberg\u00a0CN, Ghith\u00a0J, Serfass\u00a0L, Schijvenaars\u00a0BJA, Sboner\u00a0A (2022) Application of artificial intelligence to overcome clinical information overload in urological cancer. BJU Int 130(3):291\u2013300. https:\/\/doi.org\/10.1111\/bju.15662","journal-title":"BJU Int"},{"key":"2831_CR3","unstructured":"K\u00fcnstliche Intelligenz | K\u00fcnstliche Intelligenz in chirurgischen Disziplinen: Einsatz, Nutzen und Potenzial \u2013 ein Delphi-Expertenkonsensus | springermedizin.de. Accessed February\u00a09, 2026. https:\/\/www.springermedizin.de\/kuenstliche-intelligenz\/urologie\/kuenstliche-intelligenz-in-chirurgischen-disziplinen-einsatz-nut\/52001088"},{"key":"2831_CR4","unstructured":"Duwe G, H\u00f6fner T. (2024) K\u00fcnstliche Intelligenz und Digitalisierung: Perspektiven und Herausforderungen https:\/\/www.universimed.com\/ch\/article\/hno\/kuenstliche-intelligenz-digitalisierung-perspektiven-herausforderungen-360164#!"},{"issue":"3","key":"2831_CR5","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1111\/bju.15495","volume":"128","author":"DA Winters","year":"2021","unstructured":"Winters\u00a0DA, Soukup\u00a0T, Sevdalis\u00a0N, Green\u00a0JSA, Lamb\u00a0BW (2021) The cancer multidisciplinary team meeting: in need of change? History, challenges and future perspectives. BJU Int 128(3):271\u2013279. https:\/\/doi.org\/10.1111\/bju.15495","journal-title":"BJU Int"},{"key":"2831_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2025.115367","volume":"220","author":"G Duwe","year":"2025","unstructured":"Duwe\u00a0G, Mercier\u00a0D, Kauth\u00a0V et\u00a0al (2025) Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences. Eur J Cancer 220:115367. https:\/\/doi.org\/10.1016\/j.ejca.2025.115367","journal-title":"Eur J Cancer"},{"key":"2831_CR7","doi-asserted-by":"publisher","unstructured":"Duwe G, Mercier D, Kauth V et\u00a0al (2026) Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels. https:\/\/doi.org\/10.21203\/rs.3.rs-8747701\/v1","DOI":"10.21203\/rs.3.rs-8747701\/v1"},{"issue":"8","key":"2831_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/bco2.70066","volume":"6","author":"M Sondermann","year":"2025","unstructured":"Sondermann\u00a0M, Glaser\u00a0H, Rentsch\u00a0A et\u00a0al (2025) A machine learning algorithm for automatic tumour board recommendations in prostate cancer patients. BJUI Compass 6(8):e70066. https:\/\/doi.org\/10.1002\/bco2.70066","journal-title":"BJUI Compass"},{"issue":"11","key":"2831_CR9","doi-asserted-by":"publisher","first-page":"3755","DOI":"10.1111\/cas.16327","volume":"115","author":"S Tang","year":"2024","unstructured":"Tang\u00a0S, Zhang\u00a0H, Liang\u00a0J et\u00a0al (2024) Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter. Cancer Sci 115(11):3755\u20133766. https:\/\/doi.org\/10.1111\/cas.16327","journal-title":"Cancer Sci"},{"issue":"1","key":"2831_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38162-1","volume":"13","author":"JH Han","year":"2023","unstructured":"Han\u00a0JH, Lee\u00a0S, Lee\u00a0B et\u00a0al (2023) Explainable ML models for a deeper insight on treatment decision for localized prostate cancer. Sci Rep 13(1):11532. https:\/\/doi.org\/10.1038\/s41598-023-38162-1","journal-title":"Sci Rep"},{"issue":"Suppl 2","key":"2831_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02660-7","volume":"24","author":"WH Song","year":"2024","unstructured":"Song\u00a0WH, Park\u00a0M (2024) RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning. BMC Med Inform Decis Mak 24(Suppl 2):259. https:\/\/doi.org\/10.1186\/s12911-024-02660-7","journal-title":"BMC Med Inform Decis Mak"},{"key":"2831_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.esmorw.2024.100078","author":"MJ Hetz","year":"2024","unstructured":"Hetz\u00a0MJ, Carl\u00a0N, Haggenm\u00fcller\u00a0S et\u00a0al (2024) Superhuman performance on urology board questions using an explainable language model enhanced with European Association of Urology guidelines. ESMO Real World Data Digit Oncol. https:\/\/doi.org\/10.1016\/j.esmorw.2024.100078","journal-title":"ESMO Real World Data Digit Oncol"},{"key":"2831_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2025.116168","volume":"233","author":"N Carl","year":"2026","unstructured":"Carl\u00a0N, Hetz\u00a0MJ, Wies\u00a0C et\u00a0al (2026) Enhancing clinicians\u2019 trust in large language models via transparent source attribution: A\u00a0randomized controlled evaluation in uro-oncology. Eur J Cancer 233:116168. https:\/\/doi.org\/10.1016\/j.ejca.2025.116168","journal-title":"Eur J Cancer"},{"key":"2831_CR14","doi-asserted-by":"publisher","DOI":"10.2196\/78393","volume":"27","author":"JYM Tung","year":"2025","unstructured":"Tung\u00a0JYM, Le\u00a0Q, Yao\u00a0J et\u00a0al (2025) Performance of Retrieval-Augmented Generation Large Language Models in Guideline-Concordant Prostate-Specific Antigen Testing: Comparative Study With Junior Clinicians. J\u00a0Med Internet Res 27:e78393. https:\/\/doi.org\/10.2196\/78393","journal-title":"J Med Internet Res"},{"issue":"2","key":"2831_CR15","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1245\/s10434-025-18492-2","volume":"33","author":"R Li","year":"2026","unstructured":"Li\u00a0R, Zhao\u00a0A, Peng\u00a0L et\u00a0al (2026) Evaluating the Clinical Competence of Large Language Models in Prostate Cancer Management: A Comparative Study of DeepSeek-R1 and ChatGPT. Ann Surg Oncol 33(2):1858\u20131869. https:\/\/doi.org\/10.1245\/s10434-025-18492-2","journal-title":"Ann Surg Oncol"},{"issue":"1","key":"2831_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00345-024-05423-1","volume":"43","author":"P Kaiser","year":"2025","unstructured":"Kaiser\u00a0P, Yang\u00a0S, Bach\u00a0M et\u00a0al (2025) The interaction of structured data using openEHR and large Language models for clinical decision support in prostate cancer. World J Urol 43(1):67. https:\/\/doi.org\/10.1007\/s00345-024-05423-1","journal-title":"World J Urol"},{"issue":"4","key":"2831_CR17","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1111\/bju.16240","volume":"133","author":"J Gabriel","year":"2024","unstructured":"Gabriel\u00a0J, Gabriel\u00a0A, Shafik\u00a0L, Alanbuki\u00a0A, Larner\u00a0T (2024) Artificial intelligence in the urology multidisciplinary team meeting: can ChatGPT suggest European Association of Urology guideline-recommended prostate cancer treatments? BJU Int 133(4):407\u2013409. https:\/\/doi.org\/10.1111\/bju.16240","journal-title":"BJU Int"},{"key":"2831_CR18","doi-asserted-by":"crossref","unstructured":"Rinderknecht E, Haas M, Schnabel MJ, et\u00a0al. Benchmarking Large Language Models Against Multidisciplinary Tumor Boards in Urological Oncology: Results from the Blinded, Prospective CONCORDIA Study. Eur Urol Oncol. Published online December\u00a010, 2025:S2588\u20139311(25)00326\u20118. https:\/\/doi.org\/10.1016\/j.euo.2025.11.016","DOI":"10.1016\/j.euo.2025.11.016"},{"key":"2831_CR19","doi-asserted-by":"publisher","unstructured":"Rocks JW, Mehta P (2022) Memorizing without overfitting: Bias, variance, and interpolation in overparameterized models. Phys Rev Res. 2022 Mar-May;4(1):013201. https:\/\/doi.org\/10.1103\/physrevresearch.4.013201","DOI":"10.1103\/physrevresearch.4.013201"}],"container-title":["Die Urologie"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00120-026-02831-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00120-026-02831-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00120-026-02831-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:25:34Z","timestamp":1780496734000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00120-026-02831-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,13]]},"references-count":19,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2831"],"URL":"https:\/\/doi.org\/10.1007\/s00120-026-02831-6","relation":{},"ISSN":["2731-7064","2731-7072"],"issn-type":[{"value":"2731-7064","type":"print"},{"value":"2731-7072","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,13]]},"assertion":[{"value":"2 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Einhaltung ethischer Richtlinien"}},{"value":"L.M.\u00a0Jost, G.\u00a0Duwe, V.\u00a0Kauth, T.\u00a0H\u00f6fner, M.\u00a0Glienke, M.\u00a0G\u00f6rtz, S.\u00a0Mehralivand, H.\u00a0Borgmann und J.P.\u00a0Struck geben an, dass kein Interessenkonflikt besteht.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Interessenkonflikt"}},{"value":"F\u00fcr diesen Beitrag wurden von den Autor\/-innen keine Studien an Menschen oder Tieren durchgef\u00fchrt. F\u00fcr die aufgef\u00fchrten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Interessenkonflikt"}}]}}