ArticleFrontiers in public health2026
Safety and quality of public chatbots for lung cancer prognostic information: a comparative evaluation.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
9 authors.
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Abstract
Objective: To compare the safety, accuracy, empathy, reliability, information quality, and readability of five publicly accessible large language model chatbots when answering patient-facing lung cancer prognostic questions under standardized single-turn English prompting. Methods: In this Chatbot Health Advice Reporting Transparency-guided cross-sectional evaluation, 53 standardized English prompts were submitted once to ChatGPT, Gemini, Copilot, DeepSeek, and Doubao through official web interfaces during April 1-21, 2026. Five blinded raters assessed 265 responses for safety, accuracy, empathy, DISCERN, EQIP, JAMA benchmark criteria, Global Quality Scale, and readability. Paired repeated-measures analyses were used. Results: Inter-rater agreement was good to excellent. Safety differed significantly across models (Cochran's Q = 14.089, df = 4, Conclusion: Public-facing chatbots differed substantially in safety, reliability, communication quality, and readability. These findings are time-, interface-, and prompt-dependent. Chatbots may support general patient education but should not replace individualized clinician-led prognostic communication.
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