aceTRV-2026-1148Version 2 · Revised
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Direction audit: the problem line stated no AI consequence and was removed. Filed as context; not counted as a gain or a problem.
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TRUVACE RECORD VERSION record: TRV-2026-1148 version: 2 kind: revised reason: Direction audit: the problem line stated no AI consequence and was removed. Filed as context; not counted as a gain or a problem. timestamp: 2026-10-26T06:38:43.464996Z status: published lens: general sector: health headline: Do bots provide correct and adequate guidance regarding acidity: A blinded comparison rated by patients and physicians dek: Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. Aim To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease). Methods Thirty-nine frequently asked questions were submitted to each model. Responses w… gain_title: (none) problem_title: Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. trace_subject: (none) gain_reading: (none) gain_evidence: (none) problem_reading: Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. problem_evidence: (none) quick_read: Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. Aim To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease). Responses were independently rated by three gastroenterologists for accuracy, comprehensiveness, empathy, and actionability; and by 20 patients for empathy, comprehensiveness, actionability, compassion, and usefulness. limitation: tag: Context key_points: Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. | Aim To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease). | Methods Thirty-nine frequently asked questions were submitted to each model. rundown: Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs. Aim To assess the responses generated by ChatGPT-5, Gemini-2.5, and Claude-4 for common patient questions on "acidity" (heartburn/dyspepsia/gastroesophageal reflux disease). Methods Thirty-nine frequently asked questions were submitted to each model. sources: - peer_reviewed | World Journal of Methodology | https://doi.org/10.5662/wjm.v16.i3.116022 | 2026-09-20 prev: 3d673ec1409fda44c919c155b6b47a2eab46c633bf04f331ea4672ff395984bd
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