ArticleJournal of pain research2026
Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability.
Article in Journal of pain research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Authors and funding
4 authors.
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Abstract
Purpose: To compare the quality, transparency, educational value, and readability of cancer pain information generated by four AI chatbots and to assess whether the outputs met prespecified readability benchmarks for patient education. Methods: This online cross-sectional comparative study was conducted on July 22, 2026. Nine unmodified Google-Trends-derived cancer-pain-related queries were submitted to ChatGPT 5.5, Microsoft Copilot, Google Gemini 3.5 Flash, and Perplexity, yielding 36 responses. Two oncology clinicians independently assessed the responses using DISCERN, Ensuring Quality Information for Patients (EQIP), Journal of the American Medical Association (JAMA) benchmark criteria, and the Global Quality Score (GQS). Six established indices assessed readability. Matched model comparisons used Friedman tests with query as the repeated unit and Kendall's W as the omnibus effect size; significant quality outcomes were followed by Holm-adjusted paired Wilcoxon signed-rank tests. Results: DISCERN did not differ significantly across models (χ Conclusion: The four chatbots differed across information quality, source transparency, educational utility, and formula-based readability. Claim-level clinical accuracy and safety were not evaluated in this study and warrant separate guideline-based assessment.
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