acePerformance of four large language model chatbots in responding to patient-facing questions about osteoporotic vertebral compression fractures: a cross-sectional paired evaluation
Background Large language model (LLM) chatbots are increasingly used by the public to obtain health information, but the safety and quality of their responses to questions about osteoporotic vertebral compression fractures (OVCFs) remain unclear. Objective To compare the safety and patient-facing response quality of four publicly accessible LLM chatbots when responding to standardized questions about osteoporotic vertebral compression fractures. Methods In this cross-sectional paired evaluation, 36 standardized…
Clinician raters found the chatbots' answers to patient questions about osteoporotic vertebral compression fractures generally factually accurate, and ChatGPT and Gemini scored higher than DeepSeek and Doubao on empathy.
Every one of the four chatbots produced some responses that raters classified as potentially unsafe for patients asking about osteoporotic vertebral compression fractures, so the tools are not safe as standalone patient education.
The findings are specific to the access location, language, single-turn conditions and two generation dates, and should not be assumed to hold across settings or after model updates; stability across rounds was good but not complete.
Evidence
- Peer-reviewedFrontiers in Musculoskeletal Disorders2026-10-16
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Truvace Impact Record TRV-2026-1715, v1: “Performance of four large language model chatbots in responding to patient-facing questions about osteoporotic vertebral compression fractures: a cross-sectional paired evaluation.” Truvace, 2026-10-17. /record/TRV-2026-1715 (accessed at citation time). sha256 673f361f0ee1e28f…
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