ArticleJournal of cancer survivorship : research and practice2025
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.
Article in Journal of cancer survivorship : research and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- The doctors of the future: the competition of ChatGPT-4, ChatGPT-4 omni, and Gemini 2.0 Flash in andrology.BMC urology · 2026Article
- Investigating the Readability and Quality of AI Systems to Trending Questions About Food Poisoning.Journal of food science · 2026Article
- Performance evaluation of large language models in bladder cancer patient education Q&A: a cross-sectional study.Frontiers in oncology · 2026Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial intelligence (AI) is quickly transforming healthcare by improving patient and clinician access to and understanding of medical information. Generative AI models answer healthcare queries and provide tailored and quick responses. This research evaluates the readability and quality of bladder cancer (BC) patient information in 10 popular AI-enabled chatbots. MATERIALS AND
methodsWe used the latest versions of ten popular chatbots: OpenAI's GPT-4o, Microsoft's Copilot Pro, Claude-3.5 Haiku, Sonar Large, Grok 2, Gemini Advanced 1.5 Pro, Mistral Large, Google Palm 2 (Google Bard), Meta's Llama 3.3, and Meta AI v2. Prompts were developed to provide texts about BC, non-muscle-invasive BC, muscle-invasive BC, and metastatic BC. The modified Ensuring Quality Information for Patients (mEQIP), the Quality Evaluating Scoring Tool (QUEST), and DISCERN were used to assess quality. The Average Reading Level Consensus (ARLC), Flesch Reading Ease (FKRE), and Flesch-Kincaid Grade Level (FKGL) were used to evaluate readability.
resultsTen chatbots exhibited statistically significant differences in mean mEQIP, DISCERN, and QUEST scores (p = 0.048, p = 0.025, and p = 0.021, respectively). Meta scored lowest on the average mEQIP, DISCERN, and QUEST, while Llama attained the highest. Statistically significant differences were also seen in the chatbots' average ARLC, FKGL, and FKRE scores (p = 0.002, p = 0.001, and p = 0.002, respectively), in which Google Palm produced texts that are easiest to read, and Llama is the most difficult chatbot to understand.
conclusionAI chatbots can produce information on BC that is of moderate quality and readability, while there is significant variability among platforms. Results should be evaluated with caution due to the single-query approach and the continuously advancing AI models. Clinicians can support safety in implementation by delivering structured feedback and incorporating content review stages into patient education processes. Continuous collaboration between healthcare practitioners and AI developers is crucial to maintain the accuracy, currency, and clarity of AI-generated content.
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41258614What 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.