ArticleInternational journal of impotence research2025
Evaluating AI chatbots in penis enhancement information: a comparative analysis of readability, reliability and quality.
Article in International journal of impotence research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Optimizing Large Language Model Responses to Medical Queries: a Cross-sectional Study On the Effective Use of Chatgpt for Cancer-related Questions.Journal of medical systems · 2026Article
- Artificial Intelligence Chatbots in Peritoneal Dialysis Education: A Cross-Sectional Comparative Study of Quality, Readability, and Reliability.Journal of clinical medicine · 2026Article
- Readability and quality assessment of AI-powered chatbot responses to overactive bladder patient questions: a comparative study.BMC urology · 2026Article
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5 authors.
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Abstract
This study aims to evaluate and compare the performance of artificial intelligence chatbots by assessing the reliability and quality of the information they provide regarding penis enhancement (PE). Search trends for keywords related to PE were determined using Google Trends ( https://trends.google.com ) and Semrush ( https://www.semrush.com ). Data covering a ten-year period was analyzed, taking into account regional trends and changes in search volume. Based on these trends, 25 questions were selected and categorized into three groups: general information (GI), surgical treatment (ST) and myths/misconceptions (MM). These questions were posed to three advanced chatbots: ChatGPT-4, Gemini Pro and Llama 3.1. Responses from each model were analyzed for readability using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease Score (FRES), while the quality of the responses was evaluated using the Ensuring Quality Information for Patients (EQIP) tool and the Modified DISCERN Score. All chatbot responses exhibited difficulty in readability and understanding according to FKGL and FRES, with no statistically significant differences among them (FKGL: p = 0.167; FRES: p = 0.366). Llama achieved the highest median Modified DISCERN score (4 [IQR:1]), significantly outperforming ChatGPT (3 [IQR:0]) and Gemini (3 [IQR:2]) (p < 0.001). Pairwise comparisons showed no significant difference between ChatGPT and Gemini (p = 0.070), but Llama was superior to both (p < 0.001). In EQIP scores, Llama also scored highest (73.8 ± 2.2), significantly surpassing ChatGPT (68.7 ± 2.1) and Gemini (54.2 ± 1.3) (p < 0.001). Across categories, Llama consistently achieved higher EQIP scores (GI:71.1 ± 1.6; ST: 73.6 ± 4.1; MM: 76.3 ± 2.1) and Modified DISCERN scores (GI:4 [IQR:0]; ST:4 [IQR:1]; MM:3 [IQR:1]) compared to ChatGPT (EQIP: GI:68.4 ± 1.1; ST: 65.7 ± 2.2; MM:71.1 ± 1.7; Modified DISCERN: GI:3 [IQR:1]; ST:3 [IQR:1]; MM:3 [IQR:0]) and Gemini (EQIP: GI:55.2 ± 1.4; ST:55.2 ± 1.6; MM:2.6 ± 2.5; Modified DISCERN: GI:1 [IQR:2]; ST:1 [IQR:2]; MM:3 [IQR:0]) (p < 0.001). This study highlights Llama's superior reliability in providing PE-related health information, though all chatbots struggled with readability.
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