Journal of pain research
Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability
By Li Q, Zhang S, Ma X and 1 others.
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.