Evidence map›Paper›PMID 41257612›Full record

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.

Prachi Sharma, Bharti Sidhu, Ashwini Reddy, Puja Saxena, Ritu Gupta, Ravneet Kaur Gill, Ananya Ray, Rajeev Chauhan

Abstract readObservational StudyComparative Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Prachi SharmaDepartment of Neuroanaesthesiology and Neurointensive Care, Max Superspeciality Hospital, Saket, New Delhi, India.
Bharti SidhuDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India.
Ashwini ReddyDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India. ashreddy02@gmail.com.
Puja SaxenaDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India.
Ritu GuptaDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India.
Ravneet Kaur GillDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India.
Ananya RayDepartment of Anesthesia and Intensive Care, Dr B.R. Ambedkar Institute of Medical Sciences, Punjab, Mohali, India.
Rajeev ChauhanDepartment of Anesthesia and Intensive Care, Post Graduate Institute of Medical Education and Research, Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AnesthesiaArtificial IntelligenceLanguagePatient Education as TopicAdultAnesthesiologyCholecystectomy, LaparoscopicComprehensionFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsMaleMiddle AgedProspective StudiesArtificial intelligenceChatGPTGoogle geminiNatural language processingPreanesthetic education

Identifiers

PMID41257612
PMCPMC12628519

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.