Artificial Intelligence(AI) in the Emergency Department (ED) in Cologne

Condition(s) studied

Study summary

This retrospective, non-interventional study evaluates the prognostic performance of open-weight Large Language Models (LLMs) in the setting of a German academic emergency department. Using a full census of all consecutive emergency department cases at University Hospital Cologne between 01 January 2023 and 31 December 2025 (approximately 100,000 cases), the study assesses whether LLMs can make reliable prognostic predictions (e.g., hospital admission, imaging, diagnosis, placement) based on the initial history, vital signs, and triage category. In addition, it quantifies how strongly automated anonymization and perturbation procedures affect the models' diagnostic accuracy. This is an Investigator-Initiated Trial (IIT) with no intervention on patients.

Eligibility

Primary outcome measure(s)

• Diagnostic accuracy of the LLM predictions (AUROC, F1 score) compared with the clinical gold standard — From enrollment to the end of retrospective observation period at 1 year
  assessment at the level of the individual emergency department encounter).\]

Trial sites (1)

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