{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T22:59:08Z","timestamp":1781045948962,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pancreatic cancer requires nuanced, multidisciplinary treatment planning typically conducted within tumor boards. While Large Language Models (LLMs) have shown capabilities in medical reasoning, their ability to approximate complex, integrative decision-making in oncology remains underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study evaluated the performance of LLaMA 3.3 (70b) in predicting tumor board decisions for newly diagnosed pancreatic cancer patients. Clinical documentation (including free-text imaging reports, pathology findings, and patient history) from 42 first-diagnosis cases discussed in a real-world tumor board was collected. The model was tasked with predicting one of three treatment options: surgical resection (SURG), neoadjuvant chemotherapy (NEO), or palliative therapy (PALL). Four prompting strategies were evaluated: zero-shot, advanced (adv.) zero-shot, Chain-of-Thought (CoT), and few-shot prompting. Performance was assessed using accuracy, micro- and macro-averaged F1 scores, and category-specific recall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The advanced zero-shot and CoT strategies achieved the highest overall accuracy of 78.6% and a micro-averaged F1 score of 0.786. However, this performance was driven primarily by the correct classification of majority classes (SURG and PALL). Crucially, both high-accuracy strategies failed to identify any of the neoadjuvant therapy candidates (Recall NEO\u2009=\u20090.00; 0\/7 cases), systematically misclassifying them as palliative or surgical. While few-shot prompting improved the detection of neoadjuvant cases (Recall NEO\u2009=\u20091.00), it introduced substantial noise, reducing overall accuracy to 56.7%. LLaMA 3.3 (70b) demonstrates high concordance with tumor board decisions for clear-cut surgical or palliative cases but exhibits a critical systematic failure in identifying candidates for neoadjuvant therapy. The high global accuracy masks a significant safety limitation regarding the recognition of complex, intermediate-stage patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest that current LLMs may approximate majority-class decisions but risk overlooking curative treatment pathways in nuanced scenarios, necessitating rigorous oversight and specific adaptation before clinical consideration<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03444-x","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:16:20Z","timestamp":1773980180000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-assisted tumor board decision-making in pancreatic oncology"],"prefix":"10.1186","volume":"26","author":[{"given":"Markus","family":"Mergen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felix","family":"Busch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Schwarberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pia","family":"Koldeweihe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Jungwirth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Sydlik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. Carlo","family":"Maurer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus R.","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Spitzl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian T.","family":"Gassert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"3444_CR1","unstructured":"Howlader N, Noone AM, Krapcho M, et al. editors. SEER Cancer Statistics Review, 1975\u20132013. National Cancer Institute; Bethesda, MD: available at http:\/\/seer.cancer.gov\/csr\/1975_2013\/, based on November 2015. SEER data submission, posted to the SEER website, April 2016."},{"issue":"4","key":"3444_CR2","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1038\/ajg.2016.610","volume":"112","author":"EL Fogel","year":"2017","unstructured":"Fogel EL, Shahda S, Sandrasegaran K, DeWitt J, Easler JJ, Agarwal DM, Eagleson M, Zyromski NJ, House MG, Ellsworth S, El Hajj I, O\u2019Neil BH, Nakeeb A, Sherman S. A Multidisciplinary Approach to Pancreas Cancer in 2016: A Review. Am J Gastroenterol. 2017;112(4):537\u201354. https:\/\/doi.org\/10.1038\/ajg.2016.610. Epub 2017 Jan 31. PMID: 28139655; PMCID: PMC5659272.","journal-title":"Am J Gastroenterol"},{"issue":"1","key":"3444_CR3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3322\/caac.21871","volume":"75","author":"RL Siegel","year":"2025","unstructured":"Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10\u201345. https:\/\/doi.org\/10.3322\/caac.21871.","journal-title":"CA Cancer J Clin"},{"issue":"16","key":"3444_CR4","doi-asserted-by":"publisher","first-page":"4648","DOI":"10.3390\/jcm11164648","volume":"11","author":"M Caban","year":"2022","unstructured":"Caban M, Ma\u0142ecka-Wojciesko E. Pancreatic Incidentaloma. J Clin Med. 2022;11(16):4648. https:\/\/doi.org\/10.3390\/jcm11164648. PMID: 36012893; PMCID: PMC9409921.","journal-title":"J Clin Med"},{"key":"3444_CR5","doi-asserted-by":"publisher","first-page":"5577","DOI":"10.3390\/cancers15235577","volume":"15","author":"M Caban","year":"2023","unstructured":"Caban M, Ma\u0142ecka-Wojciesko E. Gaps and Opportunities in the Diagnosis and Treatment of Pancreatic Cancer. Cancers. 2023;15:5577. https:\/\/doi.org\/10.3390\/cancers15235577.","journal-title":"Cancers"},{"issue":"2","key":"3444_CR6","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.pathol.2021.09.012","volume":"54","author":"OG McDonald","year":"2022","unstructured":"McDonald OG. The biology of pancreatic cancer morphology. Pathology. 2022;54(2):236\u201347. https:\/\/doi.org\/10.1016\/j.pathol.2021.09.012. Epub 2021 Dec 3. PMID: 34872751; PMCID: PMC8891077.","journal-title":"Pathology"},{"key":"3444_CR7","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1038\/s41392-021-00659-4","volume":"6","author":"S Wang","year":"2021","unstructured":"Wang S, Zheng Y, Yang F, et al. The molecular biology of pancreatic adenocarcinoma: translational challenges and clinical perspectives. Sig Transduct Target Ther. 2021;6:249. https:\/\/doi.org\/10.1038\/s41392-021-00659-4.","journal-title":"Sig Transduct Target Ther"},{"key":"3444_CR8","doi-asserted-by":"publisher","first-page":"1119557","DOI":"10.3389\/fsurg.2023.1119557","volume":"10","author":"G Quero","year":"2023","unstructured":"Quero G, De Sio D, Fiorillo C, Menghi R, Rosa F, Massimiani G, Laterza V, Lucinato C, Galiandro F, Papa V, Salvatore L, Bensi M, Tortorelli AP, Tondolo V, Alfieri S. The role of the multidisciplinary tumor board (MDTB) in the assessment of pancreatic cancer diagnosis and resectability: A tertiary referral center experience. Front Surg. 2023;10:1119557. https:\/\/doi.org\/10.3389\/fsurg.2023.1119557. PMID: 36874464; PMCID: PMC9981784.","journal-title":"Front Surg"},{"issue":"2","key":"3444_CR9","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.hpb.2016.11.002","volume":"19","author":"DG Brauer","year":"2017","unstructured":"Brauer DG, Strand MS, Sanford DE, Kushnir VM, Lim KH, Mullady DK, Tan BR Jr, Wang-Gillam A, Morton AE, Ruzinova MB, Parikh PJ, Narra VR, Fowler KJ, Doyle MB, Chapman WC, Strasberg SS, Hawkins WG, Fields RC. Utility of a multidisciplinary tumor board in the management of pancreatic and upper gastrointestinal diseases: an observational study. HPB (Oxford). 2017;19(2):133\u20139. Epub 2016 Dec 1. PMID: 27916436; PMCID: PMC5477647.","journal-title":"HPB (Oxford)"},{"key":"3444_CR10","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1200\/jco.2016.34.4_suppl.319","volume":"34","author":"G David","year":"2016","unstructured":"David G, Brauer, et al. Utility of a multidisciplinary tumor board in the management of pancreatic diseases. JCO. 2016;34:319\u2013319. https:\/\/doi.org\/10.1200\/jco.2016.34.4_suppl.319.","journal-title":"JCO"},{"key":"3444_CR11","doi-asserted-by":"publisher","first-page":"174","DOI":"10.3390\/diagnostics14020174","volume":"14","author":"S Tripathi","year":"2024","unstructured":"Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics. 2024;14:174. https:\/\/doi.org\/10.3390\/diagnostics14020174.","journal-title":"Diagnostics"},{"issue":"3","key":"3444_CR12","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3978\/j.issn.2078-6891.2011.036","volume":"2","author":"P Tummala","year":"2011","unstructured":"Tummala P, Junaidi O, Agarwal B. Imaging of pancreatic cancer: An overview. J Gastrointest Oncol. 2011;2(3):168\u201374. https:\/\/doi.org\/10.3978\/j.issn.2078-6891.2011.036. PMID: 22811847; PMCID: PMC3397617.","journal-title":"J Gastrointest Oncol"},{"issue":"24","key":"3444_CR13","doi-asserted-by":"publisher","first-page":"7864","DOI":"10.3748\/wjg.v20.i24.7864","volume":"20","author":"ES Lee","year":"2014","unstructured":"Lee ES, Lee JM. Imaging diagnosis of pancreatic cancer: a state-of-the-art review. World J Gastroenterol. 2014;20(24):7864\u201377. https:\/\/doi.org\/10.3748\/wjg.v20.i24.7864. PMID: 24976723; PMCID: PMC4069314.","journal-title":"World J Gastroenterol"},{"issue":"2","key":"3444_CR14","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1177\/0300891621999092","volume":"108","author":"G Artioli","year":"2022","unstructured":"Artioli G, Besutti G, Cassetti T, Sereni G, Zizzo M, Bonacini S, Carlinfante G, Panebianco M, Cavazza A, Pinto C, Sassatelli R, Pattacini P, Giorgi Rossi P. Impact of multidisciplinary approach and radiologic review on surgical outcome and overall survival of patients with pancreatic cancer: a retrospective cohort study. Tumori. 2022;108(2):147\u201356. Epub 2021 Mar 14. PMID: 33719770.","journal-title":"Tumori"},{"issue":"1","key":"3444_CR15","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1186\/s12885-020-06809-1","volume":"20","author":"M Freytag","year":"2020","unstructured":"Freytag M, Herrlinger U, Hauser S, Bauernfeind FG, Gonzalez-Carmona MA, Landsberg J, Buermann J, Vatter H, Holderried T, Send T, Schumacher M, Koscielny A, Feldmann G, Heine M, Skowasch D, Sch\u00e4fer N, Funke B, Neumann M, Schmidt-Wolf IGH. Higher number of multidisciplinary tumor board meetings per case leads to improved clinical outcome. BMC Cancer. 2020;20(1):355. https:\/\/doi.org\/10.1186\/s12885-020-06809-1. PMID: 32345242; PMCID: PMC7189747.","journal-title":"BMC Cancer"},{"key":"3444_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1038\/s43856-024-00717-2","volume":"5","author":"F Busch","year":"2025","unstructured":"Busch F, Hoffmann L, Rueger C, et al. Current applications and challenges in large language models for patient care: a systematic review. Commun Med. 2025;5:26. https:\/\/doi.org\/10.1038\/s43856-024-00717-2.","journal-title":"Commun Med"},{"issue":"11","key":"3444_CR17","doi-asserted-by":"publisher","first-page":"2792","DOI":"10.7150\/ijms.111780","volume":"22","author":"E Yu","year":"2025","unstructured":"Yu E, Chu X, Zhang W, Meng X, Yang Y, Ji X, Wu C. Large Language Models in Medicine: Applications, Challenges, and Future Directions. Int J Med Sci. 2025;22(11):2792\u2013801. https:\/\/doi.org\/10.7150\/ijms.111780. PMID: 40520893; PMCID: PMC12163604.","journal-title":"Int J Med Sci"},{"key":"3444_CR18","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","volume":"31","author":"K Singhal","year":"2025","unstructured":"Singhal K, Tu T, Gottweis J, et al. Toward expert-level medical question answering with large language models. Nat Med. 2025;31:943\u201350. https:\/\/doi.org\/10.1038\/s41591-024-03423-7.","journal-title":"Nat Med"},{"issue":"3","key":"3444_CR19","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s00405-024-08947-9","volume":"282","author":"M Aubreville","year":"2025","unstructured":"Aubreville M, Ganz J, Ammeling J, Rosbach E, Gehrke T, Scherzad A, Hackenberg S, Goncalves M. Prediction of tumor board procedural recommendations using large language models. Eur Arch Otorhinolaryngol. 2025;282(3):1619\u201329. https:\/\/doi.org\/10.1007\/s00405-024-08947-9. Epub 2024 Sep 13. PMID: 39266750.","journal-title":"Eur Arch Otorhinolaryngol"},{"issue":"10","key":"3444_CR20","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.3390\/jpm13101502","volume":"13","author":"S Griewing","year":"2023","unstructured":"Griewing S, Gremke N, Wagner U, Lingenfelder M, Kuhn S, Boekhoff J. Challenging ChatGPT 3.5 in Senology-An Assessment of Concordance with Breast Cancer Tumor Board Decision Making. J Pers Med. 2023;13(10):1502. https:\/\/doi.org\/10.3390\/jpm13101502. PMID: 37888113; PMCID: PMC10608120.","journal-title":"J Pers Med"},{"issue":"10","key":"3444_CR21","doi-asserted-by":"publisher","first-page":"3535","DOI":"10.3390\/jcm14103535","volume":"14","author":"B Karabu\u011fa","year":"2025","unstructured":"Karabu\u011fa B, Kara\u00e7in C, B\u00fcy\u00fckk\u00f6r M, Bayram D, Aydemir E, Kaya OB, Y\u0131lmaz ME, \u00c7am\u00f6z ES, Erg\u00fcn Y. The Role of Artificial Intelligence (ChatGPT-4o) in Supporting Tumor Board Decisions. J Clin Med. 2025;14(10):3535. https:\/\/doi.org\/10.3390\/jcm14103535. PMID: 40429531; PMCID: PMC12112035.","journal-title":"J Clin Med"},{"key":"3444_CR22","doi-asserted-by":"crossref","unstructured":"McKinney W. Data structures for statistical computing in Python. In: Proceedings of the 9th Python in Science Conference; 2010. pp. 56\u201361.","DOI":"10.25080\/Majora-92bf1922-00a"},{"key":"3444_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"3444_CR24","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"J Hunter","year":"2007","unstructured":"Hunter J. Matplotlib: A 2D graphics environment. Comput Sci Eng. 2007;9:90\u20135.","journal-title":"Comput Sci Eng"},{"key":"3444_CR25","doi-asserted-by":"crossref","unstructured":"Seabold S, Perktold J. Statsmodels: Econometric and statistical modeling with Python. In: Proceedings of the 9th Python in Science Conference; 2010.","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"3444_CR26","doi-asserted-by":"crossref","unstructured":"Waskom M. Seaborn: Statistical data visualization. J Open Source Softw. 2021;6.","DOI":"10.21105\/joss.03021"},{"issue":"2","key":"3444_CR27","doi-asserted-by":"publisher","first-page":"ooae043","DOI":"10.1093\/jamiaopen\/ooae043","volume":"7","author":"R Landman","year":"2024","unstructured":"Landman R, Healey SP, Loprinzo V, Kochendoerfer U, Winnier AR, Henstock PV, Lin W, Chen A, Rajendran A, Penshanwar S, Khan S, Madhavan S. Using large language models for safety-related table summarization in clinical study reports. JAMIA Open. 2024;7(2):ooae043. https:\/\/doi.org\/10.1093\/jamiaopen\/ooae043. PMID: 38818116; PMCID: PMC11137320.","journal-title":"JAMIA Open"},{"key":"3444_CR28","doi-asserted-by":"publisher","first-page":"102900","DOI":"10.1016\/j.artmed.2024.102900","volume":"154","author":"S Nerella","year":"2024","unstructured":"Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artif Intell Med. 2024;154:102900. https:\/\/doi.org\/10.1016\/j.artmed.2024.102900. Epub 2024 Jun 5. PMID: 38878555; PMCID: PMC11638972.","journal-title":"Artif Intell Med"},{"key":"3444_CR29","unstructured":"Bhagat N, Mackey O, Wilcox A. Large language models for efficient medical information extraction. AMIA Jt Summits Transl Sci Proc. 2024;2024:509\u2013514. PMID: 38827084; PMCID: PMC11141860."},{"key":"3444_CR30","doi-asserted-by":"publisher","unstructured":"Mergen M, Spitzl D, Ketzer C, Strenzke M, Marka AW, Makowski MR, Bressem KK, Adams LC, Gassert FT. Leveraging large language models for accurate AO fracture classification from CT text reports. J Imaging Inform Med. 2025 Jul 7. [Epub ahead of print]. https:\/\/doi.org\/10.1007\/s10278-025-01603-6. PMID: 40624390.","DOI":"10.1007\/s10278-025-01603-6"},{"key":"3444_CR31","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1016\/j.csbj.2025.05.019","volume":"27","author":"D Spitzl","year":"2025","unstructured":"Spitzl D, Mergen M, Bauer U, Jungmann F, Bressem KK, Busch F, Makowski MR, Adams LC, Gassert FT. Leveraging large language models for accurate classification of liver lesions from MRI reports. Comput Struct Biotechnol J. 2025;27:2139\u201346. PMID: 40502931; PMCID: PMC12158552.","journal-title":"Comput Struct Biotechnol J"},{"issue":"3","key":"3444_CR32","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41591-024-03423-7","volume":"31","author":"K Singhal","year":"2025","unstructured":"Singhal K, Tu T, Gottweis J, Sayres R, Wulczyn E, Amin M, Hou L, Clark K, Pfohl SR, Cole-Lewis H, Neal D, Rashid QM, Schaekermann M, Wang A, Dash D, Chen JH, Shah NH, Lachgar S, Mansfield PA, Prakash S, Green B, Dominowska E, Ag\u00fcera Y, Arcas B, Toma\u0161ev N, Liu Y, Wong R, Semturs C, Mahdavi SS, Barral JK, Webster DR, Corrado GS, Matias Y, Azizi S, Karthikesalingam A, Natarajan V. Toward expert-level medical question answering with large language models. Nat Med. 2025;31(3):943\u201350. https:\/\/doi.org\/10.1038\/s41591-024-03423-7. Epub 2025 Jan 8. PMID: 39779926; PMCID: PMC11922739.","journal-title":"Nat Med"},{"key":"3444_CR33","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1038\/s44222-023-00097-7","volume":"1","author":"S Bakhshandeh","year":"2023","unstructured":"Bakhshandeh S. Benchmarking medical large language models. Nat Rev Bioeng. 2023;1:543. https:\/\/doi.org\/10.1038\/s44222-023-00097-7.","journal-title":"Nat Rev Bioeng"},{"issue":"11","key":"3444_CR34","doi-asserted-by":"publisher","first-page":"e2343689","DOI":"10.1001\/jamanetworkopen.2023.43689","volume":"6","author":"M Benary","year":"2023","unstructured":"Benary M, Wang XD, Schmidt M, Soll D, Hilfenhaus G, Nassir M, Sigler C, Kn\u00f6dler M, Keller U, Beule D, Keilholz U, Leser U, Rieke DT. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023;6(11):e2343689. https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.43689. PMID: 37976064; PMCID: PMC10656647.","journal-title":"JAMA Netw Open"},{"issue":"1","key":"3444_CR35","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41523-023-00557-8","volume":"9","author":"V Sorin","year":"2023","unstructured":"Sorin V, Klang E, Sklair-Levy M, Cohen I, Zippel DB, Balint Lahat N, Konen E, Barash Y. Large language model (ChatGPT) as a support tool for breast tumor board. NPJ Breast Cancer. 2023;9(1):44. https:\/\/doi.org\/10.1038\/s41523-023-00557-8. PMID: 37253791; PMCID: PMC10229606.","journal-title":"NPJ Breast Cancer"},{"key":"3444_CR36","doi-asserted-by":"publisher","first-page":"e2400478","DOI":"10.1200\/PO-24-00478","volume":"8","author":"J Lammert","year":"2024","unstructured":"Lammert J, Dreyer T, Mathes S, Kuligin L, Borm KJ, Schatz UA, Kiechle M, L\u00f6rsch AM, Jung J, Lange S, Pfarr N, Durner A, Schwamborn K, Winter C, Ferber D, Kather JN, Mogler C, Illert AL, Tschochohei M. Expert-Guided Large Language Models for Clinical Decision Support in Precision Oncology. JCO Precis Oncol. 2024;8:e2400478. https:\/\/doi.org\/10.1200\/PO-24-00478. Epub 2024 Oct 30. PMID: 39475661.","journal-title":"JCO Precis Oncol"},{"key":"3444_CR37","doi-asserted-by":"publisher","first-page":"1455413","DOI":"10.3389\/fonc.2024.1455413","volume":"14","author":"B Schmidl","year":"2024","unstructured":"Schmidl B, H\u00fctten T, Pigorsch S, St\u00f6gbauer F, Hoch CC, Hussain T, Wollenberg B, Wirth M. Assessing the role of advanced artificial intelligence as a tool in multidisciplinary tumor board decision-making for recurrent\/metastatic head and neck cancer cases - the first study on ChatGPT 4o and a comparison to ChatGPT 4.0. Front Oncol. 2024;14:1455413. https:\/\/doi.org\/10.3389\/fonc.2024.1455413. PMID: 39301542; PMCID: PMC11410764.","journal-title":"Front Oncol"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03444-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03444-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03444-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T07:39:59Z","timestamp":1775720399000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03444-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3444"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03444-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]},"assertion":[{"value":"20 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was approved by the Ethics Committee of the Technical University of Munich (Approval ID: 2024-590-S-CB). The requirement for individual informed consent was waived by the ethics committee due to the retrospective design and use of fully anonymized clinical data. The study was conducted in accordance with the Declaration of Helsinki and institutional guidelines.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"During the preparation of this work the author(s) used ChatGPT5 in order to improve the readability and language of the manuscript. After using this tool\/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of generative AI and AI-assisted technologies in the writing process"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"116"}}