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

ABSTRACT: Background: Large language models (LLMs) are increasingly used in clinical medicine, yet their ability to interpret advanced intraoperative hemodynamic monitoring-particularly in the context of liver transplantation-remains largely unexplored. In this proof-of-concept study, we evaluated ChatGPT's capacity to interpret multimodal hemodynamic data derived from both standard anesthesia monitoring and the PiCCO system. The study also employed a structured assessment instrument (ARQuAT), adapted through a Delphi-based process to evaluate LLM-generated clinical interpretations. Methods: Ten key surgical-hemodynamic phases of liver transplantation were identified using a modified Delphi approach to capture the major physiological transitions of the procedure. Sequential screenshots

SUBMITTER: Kahyaoglu S 

PROVIDER: S-EPMC12841667 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature