ArticleUpdates in surgery2026
Use of generative large language models for patient education on common surgical conditions: a comparative analysis between ChatGPT and Google Gemini.
Article in Updates in surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Review
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing importance for patients to easily access information regarding their medical conditions to improve their understanding and participation in health care decisions. Artificial Intelligence (AI) has proven as a fast, efficient, and effective tool in educating patients regarding their health care conditions. The aim of the study is to compare the responses provided by AI tools, ChatGPT and Google Gemini, to assess for conciseness and understandability of information provided for the medical conditions Deep vein thrombosis, decubitus ulcers, and hemorrhoids. A cross-sectional original research design was conducted regarding the responses generated by ChatGPT and Google Gemini for the post-surgical complications of Deep vein thrombosis, decubitus ulcers, and hemorrhoids. Each response was evaluated by the Flesch-Kincaid calculator for total number of words, sentences, average words per sentence, average syllables per word, grade level, and ease score. Additionally, the similarity score was evaluated using QuillBot and reliability using a modified discern score. These results were then analyzed by the unpaired or two sample t-test to compare the averages between the two AI tools to conclude which one was superior. Chat GPT required a higher education level to understand as suggested by the higher grade levels and lower ease scores. The easiest brochure was for deep vein thrombosis which had the lowest ease score and highest grade level. ChatGPT displayed more similarity with information provided on the internet as calculated by the plagiarism calculator-Quill bot. The reliability score via the Modified Discern score showing both AI tools were similar. Although there is a difference in the various scores for each AI tool, based on the P values obtained there is not enough evidence to conclude the superiority of one AI tool over the other.
Indexed as
Identifiers
39815048What 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.