Observational studyBMC anesthesiology2025
Artificial intelligence in anesthesia: comparison of the utility of ChatGPT v/s google gemini large language models in pre-anesthetic education: content, readability and sentiment analysis.
Observational study in BMC anesthesiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Elucidating the transformative role of large language models in advancing anesthesiology education.The Journal of international medical research · 2026Review
- Quality, readability, and clinical-risk signals of default public-interface LLM responses to vascular and perioperative patient questions: a cross-sectional snapshot.Frontiers in public health · 2026Article
- Large language model chatbots as sources of pediatric anesthesia health advice: An evaluation of reliability and readability.Digital healthArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge Language Models (LLMs) such as ChatGPT and Google Gemini are increasingly explored for their potential in patient education, particularly in the perioperative setting. As text-based tools trained on extensive datasets, they can generate detailed responses to common medical queries. However, their utility in delivering reliable, understandable, and emotionally appropriate preanesthetic information remains unclear.
methodsWe conducted a prospective observational study comparing ChatGPT and Google Gemini in generating educational content for patients undergoing laparoscopic cholecystectomy. From 68 patient questions submitted by anesthesiologists, 13 high-relevance items were selected. Responses from both models were independently rated by 20 anesthesiologists using a 5-point Likert scale across four domains: accuracy, comprehensiveness, clarity, and safety. Mixed-effects ordinal regression with random intercepts for rater and question estimated odds ratios (OR) and 95% confidence intervals (CI) for ChatGPT versus Gemini. Readability was assessed using standard linguistic indices, and sentiment analysis was performed. Inter-rater reliability was evaluated using Krippendorff's α.
resultsChatGPT had significantly higher odds of receiving better scores for accuracy (OR 2.32, 95% CI 1.62-3.32, p < 0.001) and comprehensiveness (OR 2.38, 95% CI 1.67-3.37, p < 0.001), with no differences for clarity (OR 1.05, 95% CI 0.75-1.47) or safety (OR 1.01, 95% CI 0.72-1.43). Gemini generated text with greater readability, demonstrated by a lower Flesch-Kincaid Grade level (p = 0.04) and higher Flesch Reading Ease score (p = 0.04). Sentiment analysis showed Gemini responses contained a wider emotional range, while ChatGPT responses were more neutral overall. Inter-rater reliability was low across domains (Krippendorff's α 0.23-0.46).
conclusionChatGPT produced more accurate and comprehensive perioperative anesthesia information, whereas Gemini offered greater readability and emotional expressiveness. Both models may serve as adjuncts in preanesthetic patient education but are not substitutes for clinician counselling. Larger, multi-center studies incorporating direct patient testing are warranted to validate these findings.
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