Authors
Sebastian Wegmann, Till Rosenkranz, Philip Egenolf, Stavros Oikonomidis, Maximilian Weber, Maximilian Lenz
Published in
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society. Aug 17, 2026. Epub Aug 17, 2026.
Abstract
To evaluate whether large language models (LLMs) can provide accurate, complete, and audience-adapted answers to common spine-surgery-related questions for patients and family practitioners.
Ten frequently asked spine-surgery questions were collected at a level 1 trauma center and simplified linguistically. Five LLMs (ChatGPT, Claude 3.5 Sonnet, Gemini Advanced 1.5 Pro, Copilot Pro, and DeepSeek V3) were queried using zero-shot prompting with persona-specific instructions for family practitioners and middle-aged patients. Responses were assessed by spine surgeons and non-medical raters for correctness, completeness, adaptability, and empathy using five-point Likert scales. Readability was quantified using the Flesch Reading Ease Score (FRES).
All LLMs generated largely correct and usable responses. ChatGPT and Claude showed the highest correctness and completeness, particularly for practitioner-directed answers. Gemini and Copilot achieved superior readability and empathy for patient-facing responses. DeepSeek demonstrated balanced performance across all domains. Readability differed substantially between practitioner- and patient-oriented outputs.
LLMs can support communication and education following spine surgery when used with structured prompting. Clinical oversight remains essential to mitigate risks related to inaccuracies and hallucinations.
III.
PMID:
42606695
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 4
- Comments 0