{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T16:52:55Z","timestamp":1757609575583,"version":"3.44.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686158"}],"license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,3]]},"abstract":"<jats:p>Introduction: Detecting negations in clinical text is crucial for accurate documentation and decision-making. Methods: This study assesses open-source Large Language Models (LLMs) for detecting negations in German clinical discharge letters, comparing them to the rule-based approach (GeNeg) and human annotations. Results: While Llama 3.3 and Deepseek-R1 (70B) showed slight accuracy improvements, their high computational costs limit practicality compared to GeNeg. Llama 3.3 achieved the highest accuracy (.9670) and F1-score (.9620), outperforming all other models and slightly exceeding GeNeg in accuracy and F1-score. However, it required significantly more computational time (5.9 sec\/sent) when compared to GeNeg\u2019s processing time (.005 sec\/sent). Conclusion: The study results suggest hybrid approaches combining rule-based efficiency paired with LLMs\u2019 linguistic capabilities. In addition, future work should therefore optimize prompts and integrate LLMs with traditional methods to balance accuracy and efficiency.<\/jats:p>","DOI":"10.3233\/shti251374","type":"book-chapter","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:24:13Z","timestamp":1756895053000},"source":"Crossref","is-referenced-by-count":0,"title":["Prompting Is All You Need \u2013 Until It Isn\u2019t: Exploring the Limits of LLMs for Negation Detection in German Clinical Text"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1236-7398","authenticated-orcid":false,"given":"Richard","family":"Zowalla","sequence":"first","affiliation":[{"name":"Faculty for Informatics, Heilbronn University, Heilbronn, Germany"},{"name":"Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3346-9633","authenticated-orcid":false,"given":"Martin","family":"Wiesner","sequence":"additional","affiliation":[{"name":"Faculty for Informatics, Heilbronn University, Heilbronn, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2025: GMDS Illuminates Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251374","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:24:14Z","timestamp":1756895054000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"ISBN":["9781643686158"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251374","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,9,3]]}}}