ArticleInternational urology and nephrology2025
The digital dialogue on premature ejaculation: evaluating the efficacy of artificial intelligence-driven responses.
Article in International urology and nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- Readability and quality assessment of AI-powered chatbot responses to overactive bladder patient questions: a comparative study.BMC urology · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study investigated the quality and comprehensibility of responses generated by three prominent artificial intelligence-powered chatbots (ChatGPT, Gemini, and Llama) when queried about premature ejaculation (PME).
methodsA set of 25 frequently asked questions (FAQs) were identified on the basis of Google Trends and Semrush platforms. Each chatbot was prompted with these questions and their responses were analyzed via a comprehensive set of metrics. Readability was assessed via the Flesch Reading Ease (FRES) and Flesch-Kincaid Grade Level (FKGL) scores. Quality and reliability were evaluated via the modified DISCERN (mDISCERN) and Ensuring Quality Information for Patients (EQIP) scores, which assess the clarity, comprehensiveness, and trustworthiness of health information.
resultsReadability scores, as assessed by FRES and FKGL, did not significantly differ across the three chatbots. In terms of quality, the mean EQIP scores were significantly different between the models, with Llama (72.2 ± 1.1) achieving the highest scores, followed by Gemini (67.6 ± 4.5) and ChatGPT (63.1 ± 4.9) (P < 0.001). The median (interquartile range) mDISCERN scores were 2 (1) for ChatGPT, 3 (0) for Gemini, and 3 (1) for Llama (P < 0.001), indicating a significant difference in the quality of information provided by the different models.
conclusionThe three chatbots demonstrated statistically similar results in terms of readability. Llama achieved the highest EQIP score among them. Additionally, both Llama and Gemini outperformed ChatGPT in terms of mDISCERN scores.
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Identifiers
40111716What 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.