ArticleFrontiers in public health2026
Evaluating AI-generated patient education materials for endometrial cancer surgery: a comparative analysis of response quality, reliability, and readability between ChatGPT and DeepSeek models.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This study aimed to evaluate and compare the quality, reliability, and readability of patient education materials on endometrial cancer surgery generated by ChatGPT (GPT-5) and DeepSeek (R1). Materials and methods: This cross-sectional study analyzed the responses generated by ChatGPT and DeepSeek to totally 41 questions covering four domains: surgical planning, preoperative evaluation, postoperative care, and long-term follow-up. Reliability was assessed through the DISCERN and EQIP instruments, quality was evaluated by the Global Quality Score (GQS), and readability was analyzed by the Flesch Reading Ease Score (FRES), Gunning Fog Index (GFI), and Flesch-Kincaid Grade Level (FKGL). Statistical comparisons were performed by using paired Results: The two large language models (LLMs) generated education materials of comparable quality, as reflected in GQS scores (median: DeepSeek vs. ChatGPT 5.00 vs. 4.67, Conclusion: Both DeepSeek and ChatGPT can generate patient education text drafts that are commendable in their structural coherence and linguistic clarity. DeepSeek demonstrates a significant advantage in information reliability, particularly excelling in postoperative and follow-up management content. ChatGPT shows a slight edge in the readability of surgical planning sections. However, the text readability of both models exceeds the general public's health literacy level. This indicates that large language models can only serve as auxiliary tools for generating patient education materials. Their outputs must undergo review by clinical experts and readability optimization to ensure both accuracy and comprehensibility of the information.
Indexed as
Identifiers
What 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.