aceLarge language model chatbots for patient-facing erectile dysfunction information: a cross-sectional evaluation of safety, accuracy, empathy, information quality and readability
Background: Generative artificial intelligence chatbots are increasingly used for health information, including sensitive conditions, such as erectile dysfunction (ED), but their safety and quality remain uncertain. To compare six large language model chatbot interfaces for ED information across safety, accuracy, empathy, information quality, source-related transparency, readability and response length. Methods: Thirty-eight standardized ED questions were submitted once to six chatbot interfaces, yielding 228 re…
In a test of six chatbot interfaces answering 38 erectile dysfunction questions, accuracy was generally high, with a median of 4.00 out of 5 for every interface.
In a test of six chatbot interfaces answering 38 erectile dysfunction questions, raters safety-flagged 26 of 228 responses (11.4%), and empathy, information quality and readability differed across the chatbots.
Each question was submitted only once to each chatbot interface, so the results capture a single response per question rather than repeated or varied answers.
Sources
- Peer-reviewedSexual Health2026-12-17
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Truvace Impact Record TRV-2026-2384, v1: “Large language model chatbots for patient-facing erectile dysfunction information: a cross-sectional evaluation of safety, accuracy, empathy, information quality and readability.” Truvace, 2026-11-05. /record/TRV-2026-2384 (accessed on the day you copy it). sha256 1f7a0c4c6e1e1fe0…
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