Congress:
ECR25
Poster Number:
C-18397
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
A. Fink, M. Russe, A. Rau, J. Weiß, F. Bamberg, T. Stein; Freiburg/DE
Disclosures:
Anna Fink:
Nothing to disclose
Maximilian Russe:
Nothing to disclose
Alexander Rau:
Nothing to disclose
Jakob Weiß:
Nothing to disclose
Fabian Bamberg:
Nothing to disclose
Thomas Stein:
Nothing to disclose
Keywords:
Artificial Intelligence, CT, Experimental investigations, Trauma
Purpose:
To evaluate the ability of an interactive chatbot to identify optimal CT protocols and contrast phases based on unstructured clinical queries and context-specific knowledge.
Background:
Selecting personalized CT protocols and contrast phases is critical for accurate diagnoses and standardized, radiation-reduced patient care. Prior studies have demonstrated a chatbots' ability to recommend imaging protocols using American College of Radiology guidelines [1]. However, real-world clinical scenarios involve unstructured queries requiring advanced reasoning and context comprehension. This study explores the application of large language models (LLMs) in these complex scenarios.