{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,14]],"date-time":"2026-11-14T08:58:17Z","timestamp":1794646697728,"version":"4.9.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:00:00Z","timestamp":1787011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NWO","award":["EINF-11268"],"award-info":[{"award-number":["EINF-11268"]}]},{"DOI":"10.13039\/501100003061","name":"Erasmus Medisch Centrum","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["10510032120003"],"award-info":[{"award-number":["10510032120003"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"TKI-LSH","award":["LSHM22046-H036"],"award-info":[{"award-number":["LSHM22046-H036"]}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["1011100633"],"award-info":[{"award-number":["1011100633"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["1011100633"],"award-info":[{"award-number":["1011100633"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We analyzed 947 brain MRI reports from a tertiary memory clinic (2016\u20132021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free text. Metrics were computed across 10 random splits of the 947 reports.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95% CI]): medial temporal atrophy: 0.90 [0.77\u20131.00] on the left and 0.96 [0.94\u20130.99] on the right, Global Cortical Atrophy: 0.87 [0.83\u20130.91], and Fazekas: 0.94 [0.93\u20130.96]. Microbleed mentions were detected with 0.93 [0.92\u20130.95] accuracy, and infarct mentions with 0.82 [0.80\u20130.84]. Text similarity for lesion location reached 0.95 [0.95\u20130.96]. Performance was lower for numerical variables: 0.80 [0.78\u20130.82] for the number of microbleeds and 0.66 [0.63\u20130.68] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 0.92 [0.90\u20130.93] for microbleeds and 0.81 [0.77\u20130.85] for infarcts using structural similarity-based selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>The free-text format of radiology reports limits the accessibility of structured data; can LLM LLaMA 3.1 automatically structure free-text Dutch neuroradiology reports<\/jats:italic>\n                      ?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>LLaMA 3.1 accurately extracted 30 variables from Dutch neuroradiology reports, with few-shot prompting significantly improving performance compared to zero-shot prompting, particularly with similarity-based example selection<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>By enabling accurate, automated post-hoc structuring of free-text radiology reports, open-weight LLMs facilitate large-scale data reuse, improve consistency in reporting, and support clinical research and decision-making in dementia care without disrupting existing clinical workflows<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12821-z","type":"journal-article","created":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T20:43:05Z","timestamp":1787085785000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic extraction of structured information from brain MRI reports using an open-weight large language model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8991-9405","authenticated-orcid":false,"given":"Kaouther","family":"Mouheb","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amos","family":"Pomp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Manenti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romy","family":"de Haan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farog","family":"Faghir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joy","family":"Martens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harro","family":"Seelaar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Mattace- Raso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meike W.","family":"Vernooij","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank J.","family":"Wolters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Klein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther E.","family":"Bron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"12821_CR1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.240895","volume":"314","author":"S Nowak","year":"2025","unstructured":"Nowak S, Wulff B, Layer YC et al (2025) Privacy-ensuring open-weights large language models are competitive with closed-weights GPT-4o in extracting chest radiography findings from free-text reports. Radiology 314:e240895. https:\/\/doi.org\/10.1148\/radiol.240895","journal-title":"Radiology"},{"key":"12821_CR2","doi-asserted-by":"publisher","unstructured":"European Society of Radiology (ESR) (2018) ESR paper on structured reporting in radiology. Insights Imaging 9:1\u20137. https:\/\/doi.org\/10.1007\/s13244-017-0588-8","DOI":"10.1007\/s13244-017-0588-8"},{"key":"12821_CR3","doi-asserted-by":"publisher","unstructured":"Visser JJ (2024) The unquestionable marriage between AI and structured reporting. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-024-11038-2","DOI":"10.1007\/s00330-024-11038-2"},{"key":"12821_CR4","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00330-024-11107-6","volume":"35","author":"F Busch","year":"2025","unstructured":"Busch F, Hoffmann L, dos Santos DP et al (2025) Large language models for structured reporting in radiology: past, present, and future. Eur Radiol 35:2589\u20132602. https:\/\/doi.org\/10.1007\/s00330-024-11107-6","journal-title":"Eur Radiol"},{"key":"12821_CR5","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1186\/s13244-023-01560-0","volume":"14","author":"DP dos Santos","year":"2023","unstructured":"dos Santos DP, Kotter E, Mildenberger P et al (2023) ESR paper on structured reporting in radiology\u2014update 2023. Insights Imaging 14:199. https:\/\/doi.org\/10.1186\/s13244-023-01560-0","journal-title":"Insights Imaging"},{"key":"12821_CR6","doi-asserted-by":"publisher","unstructured":"Ralevski A, Taiyab N, Nossal M et al (2024) Using large language models to annotate complex cases of social determinants of health in longitudinal clinical records. Preprint at https:\/\/doi.org\/10.1101\/2024.04.25.24306380","DOI":"10.1101\/2024.04.25.24306380"},{"key":"12821_CR7","doi-asserted-by":"publisher","first-page":"e0317084","DOI":"10.1371\/journal.pone.0317084","volume":"20","author":"JA Jansen","year":"2025","unstructured":"Jansen JA, Manukyan A, Khoury NA, Akalin A (2025) Leveraging large language models for data analysis automation. PLoS One 20:e0317084. https:\/\/doi.org\/10.1371\/journal.pone.0317084","journal-title":"PLoS One"},{"key":"12821_CR8","doi-asserted-by":"publisher","unstructured":"Bhayana R (2024) Chatbots and large language models in radiology: a practical primer for clinical and research applications. Radiology. https:\/\/doi.org\/10.1148\/radiol.232756","DOI":"10.1148\/radiol.232756"},{"key":"12821_CR9","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232741","volume":"311","author":"NC Lehnen","year":"2024","unstructured":"Lehnen NC, Dorn F, Wiest IC et al (2024) Data extraction from free-text reports on mechanical thrombectomy in acute ischemic stroke using ChatGPT: a retrospective analysis. Radiology 311:e232741. https:\/\/doi.org\/10.1148\/radiol.232741","journal-title":"Radiology"},{"key":"12821_CR10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230725","volume":"307","author":"LC Adams","year":"2023","unstructured":"Adams LC, Truhn D, Busch F et al (2023) Leveraging GPT-4 for post hoc transformation of free-text radiology reports into structured reporting: a multilingual feasibility study. Radiology 307:e230725. https:\/\/doi.org\/10.1148\/radiol.230725","journal-title":"Radiology"},{"key":"12821_CR11","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.jvir.2023.11.014","volume":"35","author":"F Sasaki","year":"2024","unstructured":"Sasaki F, Tatekawa H, Mitsuyama Y et al (2024) Bridging language and stylistic barriers in IR standardized reporting: enhancing translation and structure using ChatGPT-4. J Vasc Interv Radiol 35:472\u2013475.e1. https:\/\/doi.org\/10.1016\/j.jvir.2023.11.014","journal-title":"J Vasc Interv Radiol"},{"key":"12821_CR12","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232133","volume":"311","author":"A Cozzi","year":"2024","unstructured":"Cozzi A, Pinker K, Hidber A et al (2024) BI-RADS category assignments by GPT-3.5, GPT-4, and google bard: a multilanguage study. Radiology 311:e232133. https:\/\/doi.org\/10.1148\/radiol.232133","journal-title":"Radiology"},{"key":"12821_CR13","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.241073","volume":"314","author":"CH Savage","year":"2025","unstructured":"Savage CH, Kanhere A, Parekh V et al (2025) Open-source large language models in radiology: a review and tutorial for practical research and clinical deployment. Radiology 314:e241073. https:\/\/doi.org\/10.1148\/radiol.241073","journal-title":"Radiology"},{"key":"12821_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/jamiaopen\/ooaf109","volume":"8","author":"L Builtjes","year":"2025","unstructured":"Builtjes L, Bosma J, Prokop M et al (2025) Leveraging open-source large language models for clinical information extraction in resource-constrained settings. JAMIA Open 8:ooaf109. https:\/\/doi.org\/10.1093\/jamiaopen\/ooaf109","journal-title":"JAMIA Open"},{"key":"12821_CR15","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-025-05302-0","volume":"29","author":"JD Workum","year":"2025","unstructured":"Workum JD, Volkers BWS, van de Sande D et al (2025) Comparative evaluation and performance of large language models on expert level critical care questions: a benchmark study. Crit Care 29:72. https:\/\/doi.org\/10.1186\/s13054-025-05302-0","journal-title":"Crit Care"},{"key":"12821_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.nlp.2024.100124","volume":"10","author":"ZA Nazi","year":"2025","unstructured":"Nazi ZA, Hossain MdR, Mamun FA (2025) Evaluation of open and closed-source LLMs for low-resource language with zero-shot, few-shot, and chain-of-thought prompting. Nat Lang Process J 10:100124. https:\/\/doi.org\/10.1016\/j.nlp.2024.100124","journal-title":"Nat Lang Process J"},{"key":"12821_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-46411-8","volume":"15","author":"S Sandmann","year":"2024","unstructured":"Sandmann S, Riepenhausen S, Plagwitz L, Varghese J (2024) Systematic analysis of ChatGPT, Google search and Llama 2 for clinical decision support tasks. Nat Commun 15:2050. https:\/\/doi.org\/10.1038\/s41467-024-46411-8","journal-title":"Nat Commun"},{"key":"12821_CR18","doi-asserted-by":"publisher","unstructured":"\u00c7amur E, G\u00fcne\u015f YC (2025) Evaluation of the performance of ChatGPT 4.5 in LI-RADS categorization and management suggestion: zero-shot versus few-shot prompting method. Eur J Ther. https:\/\/doi.org\/10.58600\/eurjther2699","DOI":"10.58600\/eurjther2699"},{"key":"12821_CR19","doi-asserted-by":"publisher","unstructured":"Cahyawijaya S, Lovenia H, Fung P (2024) LLMs are few-shot in-context low-resource language learners. In: Duh K, Gomez H, Bethard S (eds) Proceedings of the 2024 conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, vol 1 (long papers). Association for Computational Linguistics, Mexico City. https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.24","DOI":"10.18653\/v1\/2024.naacl-long.24"},{"key":"12821_CR20","doi-asserted-by":"crossref","unstructured":"Mosbach M, Pimentel T, Ravfogel S et al (2023) Few-shot fine-tuning vs. in-context learning: a fair comparison and evaluation. In: Rogers A, Boyd-Graber J, Okazaki N (eds) Findings of the Association for Computational Linguistics: ACL 2023. Association for Computational Linguistics, Toronto, pp 12284\u201312314","DOI":"10.18653\/v1\/2023.findings-acl.779"},{"key":"12821_CR21","doi-asserted-by":"publisher","unstructured":"Grattafiori A, Dubey A, Jauhri A et al (2024) The Llama 3 herd of models. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2407.21783","DOI":"10.48550\/arXiv.2407.21783"},{"key":"12821_CR22","doi-asserted-by":"publisher","unstructured":"Meeus M, Rath\u00e9 A, Remy F et al (2024) ChocoLlama: lessons learned from teaching llamas Dutch. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2412.07633","DOI":"10.48550\/arXiv.2412.07633"},{"key":"12821_CR23","doi-asserted-by":"publisher","unstructured":"Tiedemann J, Aulamo M, Bakshandaeva D et al (2024) Democratizing neural machine translation with OPUS-MT. Lang Resour Eval 58:713\u2013755. https:\/\/doi.org\/10.1007\/s10579-023-09704-w","DOI":"10.1007\/s10579-023-09704-w"},{"key":"12821_CR24","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: sentence embeddings using Siamese BERT networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"12821_CR25","doi-asserted-by":"publisher","unstructured":"Delobelle P, Winters T, Berendt B (2020) RobBERT: a Dutch RoBERTa-based language model. In: Cohn T, He Y, Liu Y (eds) Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, pp 3255\u20133265. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.292","DOI":"10.18653\/v1\/2020.findings-emnlp.292"},{"key":"12821_CR26","unstructured":"Zhang T, Kishore V, Wu F et al (2020) BERTScore: evaluating text generation with BERT. In the 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26\u201330, 2020. https:\/\/openreview.net\/forum?id=SkeHuCVFDr"},{"key":"12821_CR27","doi-asserted-by":"publisher","unstructured":"Lachenbruch PA (2014) McNemar test. In: Wiley StatsRef: statistics reference online. John Wiley and Sons, Ltd. https:\/\/doi.org\/10.1002\/9781118445112.stat04876","DOI":"10.1002\/9781118445112.stat04876"},{"key":"12821_CR28","first-page":"144","volume":"11","author":"B Shabankhani","year":"2020","unstructured":"Shabankhani B (2020) Assessing the inter-rater reliability for nominal, categorical and ordinal data in medical sciences. Arch Pharm Pract 11:144\u2013148","journal-title":"Arch Pharm Pract"},{"key":"12821_CR29","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med 22:276\u2013282. https:\/\/doi.org\/10.11613\/BM.2012.031","journal-title":"Biochem Med"},{"key":"12821_CR30","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155\u2013163. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J Chiropr Med"},{"key":"12821_CR31","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1023\/A:1024068626366","volume":"52","author":"C Nadeau","year":"2003","unstructured":"Nadeau C, Bengio Y (2003) Inference for the generalization error. Mach Learn 52:239\u2013281. https:\/\/doi.org\/10.1023\/A:1024068626366","journal-title":"Mach Learn"},{"key":"12821_CR32","doi-asserted-by":"publisher","first-page":"675","DOI":"10.2307\/2279372","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675\u2013701. https:\/\/doi.org\/10.2307\/2279372","journal-title":"J Am Stat Assoc"},{"key":"12821_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5555\/1248547.1248548","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330. https:\/\/doi.org\/10.5555\/1248547.1248548","journal-title":"J Mach Learn Res"},{"key":"12821_CR34","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.nicl.2015.02.001","volume":"8","author":"LJL De Cocker","year":"2015","unstructured":"De Cocker LJL, Geerlings MI, Hartkamp NS et al (2015) Cerebellar infarct patterns: The SMART-Medea study. NeuroImage Clin 8:314\u2013321. https:\/\/doi.org\/10.1016\/j.nicl.2015.02.001","journal-title":"NeuroImage Clin"},{"key":"12821_CR35","doi-asserted-by":"publisher","unstructured":"Le T-D, Nguyen TT, Ha VN et al (2025) The impact of LoRA adapters on LLMs for clinical text classification under computational and data constraints. IEEE Access, 13, 109365\u2013109377. https:\/\/doi.org\/10.1109\/ACCESS.2025.3582037.","DOI":"10.1109\/ACCESS.2025.3582037"},{"key":"12821_CR36","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1017\/jme.2025.10132","volume":"53","author":"D Onitiu","year":"2025","unstructured":"Onitiu D, Wachter S, Mittelstadt B (2025) Walking backward to ensure risk management of large language models in medicine. J Law Med Ethics 53:454\u2013464. https:\/\/doi.org\/10.1017\/jme.2025.10132","journal-title":"J Law Med Ethics"},{"key":"12821_CR37","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1038\/s41746-025-01780-2","volume":"8","author":"M Zhu","year":"2025","unstructured":"Zhu M, Lin H, Jiang J et al (2025) Large language model trained on clinical oncology data predicts cancer progression. Npj Digit Med 8:397. https:\/\/doi.org\/10.1038\/s41746-025-01780-2","journal-title":"Npj Digit Med"},{"key":"12821_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-28767-z","volume":"15","author":"L Liu","year":"2025","unstructured":"Liu L, Lian L, Hao Y et al (2025) Human level information extraction from clinical reports with finetuned language models. Sci Rep 15:45239. https:\/\/doi.org\/10.1038\/s41598-025-28767-z","journal-title":"Sci Rep"},{"key":"12821_CR39","unstructured":"Lewis P, Perez E, Piktus A et al (2020) Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in neural information processing systems. Curran Associates, Inc., pp 9459\u20139474"},{"key":"12821_CR40","doi-asserted-by":"publisher","unstructured":"Spiess C, Vaziri M, Mandel L, Hirzel M (2025) AutoPDL: automatic prompt optimization for LLM agents. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2504.04365","DOI":"10.48550\/arXiv.2504.04365"},{"key":"12821_CR41","unstructured":"Keicher M, Zaripova K, Czempiel T et al (2024) FlexR: few-shot classification with language embeddings for structured reporting of chest X-rays. In: Medical imaging with deep learning. PMLR, pp 1493\u20131508"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12821-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12821-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12821-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T20:43:09Z","timestamp":1787085789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12821-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,18]]},"references-count":41,"alternative-id":["12821"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12821-z","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,18]]},"assertion":[{"value":"29 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The scientific guarantor of this publication is Esther E. Bron.","order":1,"name":"Ethics","label":"Guarantor","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors of this manuscript declare no relationship with any companies whose products or services may be related to the subject matter of the article.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Statistical analysis included MCCV, corrected resampled\n                      t\n                      -tests for CI estimation, and non-parametric comparisons using the Friedman and Nemenyi tests. Statistical consultation was obtained to ensure methodological appropriateness.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained.","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"None.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}