Feasibility evaluation of large language models in anesthesia-specific post-operative care instructions for total knee arthroplasty.

ABSTRACT:

Objective

Large language models (LLMs) are increasingly applied in medicine, but their role in peri-operative education is underexplored. This pilot feasibility study compared four LLMs in producing post-operative care instructions for total knee arthroplasty (TKA).

Methods

OpenAI GPT-4o, Claude 3.7 Sonnet, DeepSeek R1, and Gemini 2.0 Flash generated instructions from a standardized prompt. Outputs were scored (0 = does not meet, 1 = partially meets, 2 = fully meets) for accuracy, clarity, relevance, consistency, and readability. Accuracy was benchmarked against ERAS, ASA guidelines, and UpToDate. Readability was assessed using Flesch-Kincaid indices.

Results

Within this limited sample, Claude, GPT-4o, and DeepSeek R1 demonstrated higher observed accuracy than Gemini,

SUBMITTER: Nagesh D 

PROVIDER: S-EPMC12704057 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature