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Utilizing the CHART framework, this cross-sectional audit assessed five platforms, specifically isolating retrieval-augmented generation (RAG) architectures against standard pre-trained Large Language Models (LLMs). We extracted 11 high-volume patient queries from Google Trends and administered them via a zero-shot protocol. Two blinded neurosurgeons then scored the outputs against the 2023 Amsterdam Consensus Statement using four validated instruments: DISCERN and EQIP to evaluate treatment and information quality, GQS for global content quality, and JAMA benchmarks for transparency. Reliability metrics diverged significantly across models (DISCERN and EQIP,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Perplexity Pro secured the highest DISCERN (47.36\u2009\u00b1\u20094.84) and EQIP (65.00\u2009\u00b1\u20095.48) values, statistically surpassing foundational models like ChatGPT and Gemini (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01) \u2014 a performance gap likely driven by its RAG design. In contrast, GQS scores did not differ significantly across models (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.373), and JAMA-based transparency remained uniformly low (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001). Readability was assessed using six standard indices (FRES, FKGL, GFI, CLI, ARI, and SMOG), revealing that all models exceeded the 6th-grade reading level; most surpassed 10th-grade, with Perplexity Pro lowest at FKGL\u2009=\u20097.46. Although retrieval-augmented systems improve clinical accuracy, current iterations fail to provide transparent or readable advice. Clinical integration therefore requires rigorous human-in-the-loop verification and a shift toward plain-language algorithm optimization.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-51281-9","type":"journal-article","created":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T05:23:48Z","timestamp":1777785828000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reliability and readability of five AI chatbots for concussion health advice across retrieval augmented and pretrained models"],"prefix":"10.1038","volume":"16","author":[{"given":"Hefang","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caihong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,3]]},"reference":[{"key":"51281_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s11916-024-01333-4","volume":"29","author":"Y Sergeyenko","year":"2025","unstructured":"Sergeyenko, Y., Andreae, M. 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