ArticleJournal of medical systems2025
An Exploratory Comparison of AI Models for Preoperative Anesthesia Planning: Assessing ChatGPT-4o, Claude 3.5 Sonnet, and ChatGPT-o1 in Clinical Scenario Analysis.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This exploratory study examined the effectiveness of ChatGPT-4o, Claude 3.5 Sonnet, and ChatGPT-o1 in developing anesthesia plans for critical cases. Personalized anesthesia plans are essential for ensuring surgical safety and patient satisfaction. These artificial intelligence (AI) models can understand and generate anesthesia-related information. The study included a panel of five anesthesia experts, each with over ten years of experience. They qualitatively and quantitatively assessed the capabilities of the three models in formulating anesthesia plans for critical cases. The results showed no significant differences in the response quality, relevance, and applicability scores among the models; however, variations were observed in the error types and severity. ChatGPT-o1 surpassed the other models in terms of content relevance and information accuracy, demonstrating a lower error rate and higher suitability for clinical application. As an initial investigation in this rapidly evolving field, this research provides preliminary insights while acknowledging the need for further validation in clinical settings before implementation.
Indexed as
Identifiers
40810794What OpenQuestion holds
Registered trials
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.