Human-in-the-Loop Performance of LLM-Assisted Arterial Blood Gas Interpretation: A Single-Center Retrospective Study.

ABSTRACT: Background and Objectives: Interpreting acid-base disorders is challenging, particularly in complex or mixed cases. Given the growing potential of large language models (LLMs) to assist in cognitively demanding tasks, this study evaluated their performance in interpreting arterial blood gas (ABG) results. Materials and Methods: In this single-center retrospective study, 200 ABG datasets were curated to include 40 cases in each of five diagnostic categories: metabolic acidosis, respiratory acidosis, metabolic alkalosis, respiratory alkalosis, and no acid-base disorder. Three medical students, each assigned to one LLM (ChatGPT GPT-4o, Copilot GPT-4, or Gemini 1.5-flash/2.5-flash), perform ABG interpretation using two evaluation methods: interpretation (LLM-I) and interpretation

SUBMITTER: Ayala-De la Cruz S 

PROVIDER: S-EPMC12470526 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature