Evaluating AI-generated patient education materials for endometrial cancer surgery: a comparative analysis of response quality, reliability, and readability between ChatGPT and DeepSeek models.

PurposeThis 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 methodsThis 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 t-tests and Wilcoxon signed-rank tests.ResultsThe 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, p = 0.077). DeepSeek demonstrated statistically significantly higher reliability scores on both DISCERN and EQIP instruments (both p < 0.001). Readability scores (FRES, GFI) were similar between groups, while DeepSeek exhibited a higher FKGL (10.28 vs. 8.84, p < 0.001), indicating the greater text complexity. Subgroup analysis showed that DeepSeek performed better in terms of reliability in the postoperative care and long-term follow-up domains, while ChatGPT exhibited better readability in the surgical planning domain.ConclusionBoth 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.