Truace
Subscribe

A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0857· cite & verify
A large language models-assisted and expert-corrected workflow for preoperative anesthesia assessment drafts: A single-centre exploratory feasibility study
A proposal for the transfer of a large force management expert system (FRESH) from the CINCPACFLT Command Center to the CINCLANTFLT Command Center/ by Luigart, Craig Blaine.. Public domain

In brief

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.

A large language model-based artificial intelligence system, DeepSeek-R1, was used to generate structured draft assessments from de-identified case information and a standardized prompt. The original LLM-generated drafts were independently reviewed by three experienced anesthesiologists for information completeness, scientific plausibility, and focus on key risk factors.

Main points

  1. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.
  2. Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions after reviewing expert-corrected materials.
  3. We retrospectively selected 15 complex preoperative anesthesia consultation cases from The First Affiliated Hospital, Zhejiang University School of Medicine.

The problem

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.

The rundown

Secondary aims were to describe error patterns identified by anesthesiologists and to explore residents' perceptions after reviewing expert-corrected materials. We retrospectively selected 15 complex preoperative anesthesia consultation cases from The First Affiliated Hospital, Zhejiang University School of Medicine.

Sources

  1. Peer-reviewedMedicina Clínica2026-08-21

The debate