paperAugust 2026UnreviewedOpen access

SCENARIO-BASED PREHOSPITAL TRIAGE OF CARBON MONOXIDE POISONING: COMPARING FIVE LARGE LANGUAGE MODELS WITH EMERGENCY MEDICAL SERVICES PERSONNEL IN A PROSPECTIVE STUDY

Vildan Özer, Özlem Bülbül, Efnan Bayrak Erbolukbas, Serdar Karakullukçu, Aynur Şahin

Kırıkkale Üniversitesi Tıp Fakültesi Dergisi

Abstract

Objective: Carbon monoxide (CO) poisoning requires rapid identification and timely decisions regarding the need for hyperbaric oxygen therapy (HBOT) to improve clinical outcomes. This study aimed to compare the decision-making performance of emergency medical services (EMS) personnel and large language models (LLMs) in accurately determining the need for HBOT during the prehospital phase of CO poisoning cases, and to explore the potential implementation of LLMs as decision-support tools for pati

Framework mappings

OWASP Top 10 for LLM Applications

LLM04Data and Model Poisoning

MITRE ATLAS

AML.T0020Poison Training Data

Suggested from the entry's categories.

Cite

@article{ozer2026scenariobased,
  title = {{SCENARIO-BASED PREHOSPITAL TRIAGE OF CARBON MONOXIDE POISONING: COMPARING FIVE LARGE LANGUAGE MODELS WITH EMERGENCY MEDICAL SERVICES PERSONNEL IN A PROSPECTIVE STUDY}},
  author = {Vildan Özer and Özlem Bülbül and Efnan Bayrak Erbolukbas and Serdar Karakullukçu and Aynur Şahin},
  year = {2026},
  month = aug,
  journal = {Kırıkkale Üniversitesi Tıp Fakültesi Dergisi},
  doi = {10.24938/kutfd.1922944},
  url = {https://www.semanticscholar.org/paper/ed91854853fa63aa344cdee6370d9016e8d2c027}
}