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January 17, 2026Journal of Clinical MedicineOpen Access

Large Language Model-Assisted Point-in-Time Interpretation of Advanced Hemodynamics in Liver Transplant Recipients: A Pilot Evaluation of Content Quality and Safety

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Authors

SKSelma KAHYAOGLUAKAbdullah KAYGISIZİAİzzet Alatli

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Overview

A pilot study assesses language model interpretations of hemodynamic data in liver transplant recipients, indicating promising quality and safety.

Key Points

  • The study aims to evaluate the ability of ChatGPT to interpret complex hemodynamic data in liver transplant recipients and assess content quality.
  • Identified ten key hemodynamic phases of liver transplantation using a Delphi approach.
  • Collected 50 screenshots of hemodynamic data from five liver transplant recipients.
  • Submitted images and clinical background to ChatGPT for interpretation.
  • Five anesthesiologists assessed the responses using ARQuAT, evaluating various content quality domains.
  • Conducted statistical analysis for performance metrics, inter-rater reliability, and internal consistency.
  • ChatGPT achieved high median scores (4.6 to 4.8) across content-quality domains, with over 90% ratings satisfactory.
  • Lower scores were noted for frames with sudden hemodynamic changes, indicating specific areas needing further study.
  • A significant floor effect on catastrophic risk was observed, with 86% ratings as 0 risk identified.
  • Internal consistency among ARQuAT domains was excellent, while inter-rater agreement was modest.

Cite This Study

KAHYAOGLU et al. (2026) studied this question.

synapsesocial.com/papers/696b2672d2a12237a9349b89https://doi.org/10.3390/jcm15020716
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