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  3. Chatgpt And Reference Intervals: A Comparative Analysis Of Repeatability In Gpt-3.5 Turbo, Gpt-4, And Gpt-4o.
  1. Home
  3. Chatgpt And Reference Intervals: A Comparative Analysis Of Repeatability In Gpt-3.5 Turbo, Gpt-4, And Gpt-4o.

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ChatGPT and reference intervals: a comparative analysis of repeatability in GPT-3.5 Turbo, GPT-4, and GPT-4o.

Annika Meyer1,2, Edgar Schömig3, Thomas Streichert2

  • 1Department of Anesthesiology and Operative Intensive Care, Faculty of Medicine and University Hospital, University Hospital Cologne, Cologne, Germany.

Frontiers in Artificial Intelligence
|December 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models like ChatGPT show promise in lab medicine but struggle with consistent reference intervals. Newer versions improve, yet variability persists, especially for unstandardized tests.

Keywords:
ChatGPTchatbotconsistencylarge language modelreference intervalrepeatability

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Laboratory Medicine and Diagnostics
  • Clinical Pathology and Informatics

Background:

  • Large language models (LLMs) offer potential for rapid clinical consultation in laboratory medicine.
  • Uncertainty exists regarding the consistency and clinical reliability of reference intervals generated by LLMs, especially without clinical context.

Purpose of the Study:

  • To evaluate the repeatability of reference interval outputs from three ChatGPT versions (GPT-3.5-Turbo, GPT-4, GPT-4o).
  • To assess model consistency by using reference interval variability as a stress test when prompts omit interval information.

Main Methods:

  • A cross-sectional study involving 726,000 chatbot requests with standardized prompts.
  • Analysis of 246,842 reference intervals across 47 laboratory parameters for consistency.
  • Statistical analysis using coefficient of variation (CV) and regression models to assess variability.

    • Main Results:

    • Average CVs for reference intervals were 26.50% (lower limit) and 15.82% (upper limit).
    • GPT-4 and GPT-4o demonstrated significantly lower CVs than GPT-3.5-Turbo.
    • Inconsistent outputs were notable for poorly standardized parameters and varied unit expressions.

    Conclusions:

    • While newer ChatGPT versions show improved repeatability, diagnostically unacceptable variability remains, particularly for unstandardized analytes.
    • Thoughtful prompt design, global standardization of lab practices, model refinement, and regulatory oversight are crucial.
    • Current AI chatbots should be limited to professional use and trained to decline interpretation without provided reference intervals.