− HEALTH Nottingham histological grading is central to breast cancer prognosis and treatment planning, but conventiona…− HEALTH Large language models (LLMs) may help organize clinical information, but their use in perioperative settings…+ SPORTS To compare the prognostic performance of an artificial intelligence (AI) model with that of experienced clini…− HEALTH Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIU…− HEALTH To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic perfor…− EDUCATION Although endoscopic training has traditionally relied on mentorship and procedural volume to assess proficien…− HEALTH Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft…− SCIENCE Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary i…
TruaceTracing the truth around AISunday, August 23, 2026
Subscribe
Health·P Space·Evidence-backed problem·Published 2026-08-23

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

Abstract: 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…

TRV-2026-0857Peer-reviewedPermanent record — 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

The quick read

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
  • 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 after reviewing expert-corrected materials.
  • We retrospectively selected 15 complex preoperative anesthesia consultation cases from The First Affiliated Hospital, Zhejiang University School of Medicine.
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

Reader signal

How should this claim be treated?

The debate