acePeriodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations
Background Periodontitis is one of the most prevalent yet preventable oral diseases, as indicated by multiple clinical and radiographic factors. As these factors are recorded in electronic health records (EHRs), their reuse offers opportunities for personalized risk assessment and targeted prevention. Predictive AI and traditional machine learning models support fragmented detection tasks but lack the integration of textual and imaging predictors. Emerging multimodal large language models (M-LLMs) show promise i…
Two of three multimodal language models produced periodontitis risk assessments and prevention plans that experts rated acceptable at rates comparable to a periodontist's.
All tested models made unacceptable errors in interpreting panoramic radiographs, and one model scored markedly lower than the periodontist and the other models.
The study used only ten vignettes and three models, and radiographic interpretation errors occurred across all of them; the authors say improvement from refined prompting or newer versions is only possible, not shown.
Evidence
- Peer-reviewedJournal of Medical Internet Research2026-10-02
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Truvace Impact Record TRV-2026-1371, v1: “Periodontitis Risk Assessment and Prevention Planning: Comparative Study of Multimodal Large Language Models and Periodontist Evaluations.” Truvace, 2026-10-14. /record/TRV-2026-1371 (accessed at citation time). sha256 446c94e0b3fdeb89…
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