Abstract
Abstract
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 in combining these data sources for clinical assessment. Evaluating the capabilities of M-LLMs and comparing them against the current clinical standard are therefore essential to determine their potential as digital assistants.
Objective
This study aimed to evaluate the ability of M-LLMs to assess periodontitis risk and suggest prevention strategies, based on EHR data and radiographic findings. Each M-LLM was individually evaluated by periodontal experts, benchmarked against other models, and compared with a periodontist as a reference.
Methods
A vignette study was conducted following TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines for the evaluation of LLMs. Ten periodontal vignettes were created, each including a panoramic radiograph and textual EHR data. Three LLMs capable of reasoning and handling multimodal data were compared to a periodontist who generated outputs manually, based on the same prompts and input data. Periodontal experts rated all outputs across 6 predefined criteria on a 5-point Likert scale. Statistical analyses evaluated overall performance per model and tested whether performance varied per model, scenario complexity, or rater.
Results
GPT o1 Pro and Claude Sonnet 4 showed strong performance, with 86.7% and 85.6% of ratings deemed acceptable—comparable to the periodontist’s output (87.8%). Gemini 2.5 Pro was rated significantly lower than both the periodontist and the other models (59.4% acceptable;
P
Conclusions
M-LLMs demonstrated strong reasoning abilities in periodontal assessment. Across all models, unacceptable elements were consistently related to errors in radiographic interpretation, though refined prompting or newer model versions may improve this. Notably, even when radiographic findings were incorrect and plaque-retentive factors were absent, outputs were still rated well, indicating that EHR data alone provide a substantial basis. For clinical applicability, M-LLMs must at least perform comparably to a periodontist and meet the quality standards set by periodontal experts—a bar that GPT and Claude appear to approach.