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Congress: ECR25
Poster Number: C-24137
Type: Poster: EPOS Radiologist (scientific)
Authorblock: D. Männle, M. Langhals, N. Santhanam, C. G. Cho, H. Wenz, C. Groden, F. Siegel, M. E. Maros; Mannheim/DE
Disclosures:
David Männle: Nothing to disclose
Martina Langhals: Nothing to disclose
Nandhini Santhanam: Nothing to disclose
Chang Gyu Cho: Nothing to disclose
Holger Wenz: Nothing to disclose
Christoph Groden: Nothing to disclose
Fabian Siegel: Nothing to disclose
Máté Elöd Maros: Consultant: Non-related consultancy EppData GmbH Consultant: Non-related consultancy Siemens Healthineers AG
Keywords: Artificial Intelligence, Computer applications, Neuroradiology brain, CT, CT-Angiography, RIS, Computer Applications-General, Technology assessment, Ischaemia / Infarction
References

Brown, Tom B., et al. “Language Models Are Few-Shot Learners.” arXiv.org, 28 May 2020, https://doi.org/10.48550/arXiv.2005.14165.

Li, C., et al. "Large Language Models Understand and Can be Enhances by Emotional Stimuli." arXiv, 14 July 2023, https://doi.org/10.48550/arXiv.2307.11760. 

Lochan, Baysal: "Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models." arXiv, 17 October 2023, https://doi.org/10.48550/arXiv.2310.10449. 

López-Úbeda, P., et al.: "Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study." International journal of medical informatics 187, 105443 (2004), https://doi.org/10.1016/j.ijmedinf.2024.105443.

Maros, Máté E., et al. “Comparative Analysis of Machine Learning Algorithms for Computer-assisted Reporting Based on Fully Automated Cross-lingual RadLex Mappings.” Scientific Reports 11 (2021), https://doi.org/10.1038/s41598-021-85016-9.

Van Veen, D., et al. "Adapted large language models can outperform medical experts in clinical text summarization." Nat Med 30, 1134–1142 (2024), https://doi.org/10.1038/s41591-024-02855-5.