Evidence map›Paper›PMID 40605845›Full record

ArticleJMIR AI2025

Performance of 3 Conversational Generative Artificial Intelligence Models for Computing Maximum Safe Doses of Local Anesthetics: Comparative Analysis.

Mélanie Suppan, Pietro Elias Fubini, Alexandra Stefani, Mia Gisselbaek, Caroline Flora Samer, Georges Louis Savoldelli

Abstract read
In one paragraph

Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

2 citing papers in PubMed.

  1. Trial
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mélanie SuppanDivision of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0002-8807-9619
Pietro Elias FubiniDivision of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0003-1371-6465
Alexandra StefaniDivision of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0002-7483-2075
Mia GisselbaekDivision of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0002-2296-8106
Caroline Flora SamerDepartment of Anaesthesiology, Pharmacology, Intensive Care and Emergency Medicine, Faculty of Medicine, University of Geneva, Rue Michel-Servet 1, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0001-8178-0616
Georges Louis SavoldelliDivision of Anaesthesiology, Department of Acute Care Medicine, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland.ORCID http://orcid.org/0000-0002-8968-6920

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generative artificial intelligence (AI) is showing great promise as a tool to optimize decision-making across various fields, including medicine. In anesthesiology, accurately calculating maximum safe doses of local anesthetics (LAs) is crucial to prevent complications such as local anesthetic systemic toxicity (LAST). Current methods for determining LA dosage are largely based on empirical guidelines and clinician experience, which can result in significant variability and dosing errors. AI models may offer a solution, by processing multiple parameters simultaneously to suggest adequate LA doses. Objective: This study aimed to evaluate the efficacy and safety of 3 generative AI models, ChatGPT (OpenAI), Copilot (Microsoft Corporation), and Gemini (Google LLC), in calculating maximum safe LA doses, with the goal of determining their potential use in clinical practice. Methods: A comparative analysis was conducted using a 51-item questionnaire designed to assess LA dose calculation across 10 simulated clinical vignettes. The responses generated by ChatGPT, Copilot, and Gemini were compared with reference doses calculated using a scientifically validated set of rules. Quantitative evaluations involved comparing AI-generated doses to these reference doses, while qualitative assessments were conducted by independent reviewers using a 5-point Likert scale. Results: All 3 AI models (Gemini, ChatGPT, and Copilot) completed the questionnaire and generated responses aligned with LA dose calculation principles, but their performance in providing safe doses varied significantly. Gemini frequently avoided proposing any specific dose, instead recommending consultation with a specialist. When it did provide dose ranges, they often exceeded safe limits by 140% (SD 103%) in cases involving mixtures. ChatGPT provided unsafe doses in 90% (9/10) of cases, exceeding safe limits by 198% (SD 196%). Copilot's recommendations were unsafe in 67% (6/9) of cases, exceeding limits by 217% (SD 239%). Qualitative assessments rated Gemini as "fair" and both ChatGPT and Copilot as "poor." Conclusions: Generative AI models like Gemini, ChatGPT, and Copilot currently lack the accuracy and reliability needed for safe LA dose calculation. Their poor performance suggests that they should not be used as decision-making tools for this purpose. Until more reliable AI-driven solutions are developed and validated, clinicians should rely on their expertise, experience, and a careful assessment of individual patient factors to guide LA dosing and ensure patient safety.

Indexed as

AIanesthesiologyanestheticsartificial intelligenceartificial intelligence modelsChatGPTcomparative analysisconversational generative artificial intelligenceCopilotdose calculationGeminigenerative artificial intelligenceLAlarge language modelLLMlocal anestheticmachine learningMLnatural language processingneural networkNLPperformancetoxicity

Identifiers

PMID40605845
PMCPMC12223683

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.