Congress:
ECR25
Poster Number:
C-11239
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
S. H. Kim, S. Schramm, L. C. Adams, R. Braren, K. Bressem, M. Keicher, C. Zimmer, D. M. Hedderich, B. Wiestler; München/DE
Disclosures:
Su Hwan Kim:
Nothing to disclose
Severin Schramm:
Nothing to disclose
Lisa C. Adams:
Nothing to disclose
Rickmer Braren:
Nothing to disclose
Keno Bressem:
Nothing to disclose
Matthias Keicher:
Nothing to disclose
Claus Zimmer:
Nothing to disclose
Dennis M Hedderich:
Nothing to disclose
Benedikt Wiestler:
Nothing to disclose
Keywords:
Artificial Intelligence, CT, MR, Technology assessment, Pathology
Our findings highlight the potential of open-source LLMs as decision support tools for radiological differential diagnosis in challenging real-world cases. The top-performing open-source model, Llama-3, delivered results nearly on par with human experts and GPT-4o, demonstrating that open-source models are rapidly narrowing the gap with proprietary counterparts.