ArticleJournal of medical systems2024
How Useful are Current Chatbots Regarding Urology Patient Information? Comparison of the Ten Most Popular Chatbots' Responses About Female Urinary Incontinence.
Article in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Chatgpt vs traditional pedagogy: a comparative study in urological learning.World journal of urology · 2025Trial
- Quality assessment of artificial intelligence responses in erectile dysfunction: a comparative study based on EAU recommendations.International journal of impotence research · 2026Article
- What Is Required for AI to Improve the Assessment and Treatment of Patients With Lower Urinary Tract Dysfunction? ICI-RS 2025.Neurourology and urodynamics · 2026Article
- Assessment of Stage Two Hypertension Treatment Plans Written by Generative AI.Journal of clinical medicine · 2026Article
- Quality and Usability of Prostate Cancer Information Generated by Artificial Intelligence Chatbots: A Comparative Analysis.Cancers · 2026Article
- ZhongdaChat-ED: a medical large language model for personalized erectile dysfunction health consultation and professional clinical decision-making using retrieval-augmented generation.Asian journal of andrology · 2026Article
- What do the current popular artificial intelligence chatbots offer us regarding patient information? Comparison of responses from the ten most popular chatbots about bladder cancer.Journal of cancer survivorship : research and practice · 2025Article
- The application of problem-based learning (PBL) guided by ChatGPT in clinical education in the Department of Nephrology.BMC medical education · 2025Article
- Artificial intelligence and patient education.Current opinion in urology · 2025Review
- Battle of the artificial intelligence: a comprehensive comparative analysis of DeepSeek and ChatGPT for urinary incontinence-related questions.Frontiers in public health · 2025Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research evaluates the readability and quality of patient information material about female urinary incontinence (fUI) in ten popular artificial intelligence (AI) supported chatbots. We used the most recent versions of 10 widely-used chatbots, including OpenAI's GPT-4, Claude-3 Sonnet, Grok 1.5, Mistral Large 2, Google Palm 2, Meta's Llama 3, HuggingChat v0.8.4, Microsoft's Copilot, Gemini Advanced, and Perplexity. Prompts were created to generate texts about UI, stress type UI, urge type UI, and mix type UI. The modified Ensuring Quality Information for Patients (EQIP) technique and QUEST (Quality Evaluating Scoring Tool) were used to assess the quality, and the average of 8 well-known readability formulas, which is Average Reading Level Consensus (ARLC), were used to evaluate readability. When comparing the average scores, there were significant differences in the mean mQEIP and QUEST scores across ten chatbots (p = 0.049 and p = 0.018). Gemini received the greatest mean scores for mEQIP and QUEST, whereas Grok had the lowest values. The chatbots exhibited significant differences in mean ARLC, word count, and sentence count (p = 0.047, p = 0.001, and p = 0.001, respectively). For readability, Grok is the easiest to read, while Mistral is highly complex to understand. AI-supported chatbot technology needs to be improved in terms of readability and quality of patient information regarding female UI.
Indexed as
Identifiers
39535651What 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.