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
The performance of open-source large language models (OS-LLMs) showed a clear and consistent improvement as the size of the model increased, as well as when a more recent iteration or version of the model was used. Additionally, providing a greater number of in-context learning samples, specifically in a 10-shot setting, further contributed to enhanced performance. Furthermore, role and task definitions that were formulated in the English language consistently led to better results compared to those that were presented in German.