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TRUVACE RECORD VERSION
record: TRV-2026-2140
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-22T01:09:44.493963Z
status: published
lens: g_space
sector: health
headline: Performance of large language models in providing patient- oriented information on gingival recession: a comparative study
dek: Patients are increasingly turning to large language models (LLMs) for health information, but the quality of patient-oriented information remains uncertain. This article evaluated the performance of three major LLMs in answering clinically relevant patient's questions related to gingival recession. A descriptive comparative study was carried out utilizing twenty standardized patient-centered questions related to gingival recession. Each question was submitted once to ChatGPT 5.5, Gemini 3.5, and Claude Sonnet 5,…
gain_title: Periodontist ratings found ChatGPT, Gemini and Claude similarly accurate and complete on gingival recession questions, suggesting they could supplement patient education.
problem_title: (none)
trace_subject: (none)
gain_reading: Periodontist ratings found ChatGPT, Gemini and Claude similarly accurate and complete on gingival recession questions, suggesting they could supplement patient education.
gain_evidence: These LLMs may be useful as adjuncts to patient education
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers put twenty standardized patient-style questions about gingival recession to three chatbots, producing 60 answers. Five board-certified periodontists scored each answer for accuracy and completeness and ranked their preferences, while word count and response time were also logged.

The results suggest patients seeking dental information from these tools may receive broadly similar quality regardless of which one they use. The authors still frame the tools as complements to professional periodontal consultation, and the study does not show how patients actually use or act on the answers.
limitation: Each question was submitted only once to each model and the evaluation covered a single topic, so the findings may not generalize to other conditions or repeated queries.
tag: Evidence-backed gain
key_points: Twenty patient-centered questions were each submitted once to three LLMs, yielding 60 responses. | Accuracy (p = 0.993) and completeness (p = 0.972) did not differ significantly among the models. | ChatGPT was fastest (median 5.82 s) and most concise (median 232 words). | Gemini and Claude were each ranked first in 38% of evaluations versus 24% for ChatGPT. | Post-treatment Care questions received the highest accuracy for all three models.
rundown: The evaluation used a descriptive comparative design. Five board-certified periodontists independently applied the validated Accuracy of Information Index and a completeness scale, and analysis used non-parametric tests and generalized estimating equations with significance set at p ≤ 0.05.

Speed and length differed markedly. ChatGPT responded in a median of 5.82 seconds, against 8.46 for Gemini and 14.61 for Claude. Median word counts were 232 for ChatGPT, 438 for Gemini and 333.5 for Claude.

Expert preference rankings differed significantly (p = 0.002), with Gemini and Claude favored more often than ChatGPT. The authors conclude the tools may help with patient education but should not replace professional periodontal consultation.
sources:
- peer_reviewed | The Saudi Dental Journal | https://doi.org/10.1007/s44445-026-00247-8 | 2026-10-19
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