Diagnostic performance of multimodal large language models in detecting pulp stones on panoramic radiographs

Background

Pulp stones are calcified structures within the dental pulp that may increase the complexity of endodontic treatment and are commonly identified through radiographic examinations. Despite recent advances in artificial intelligence applied to dental imaging, the diagnostic capability of publicly available multimodal large language models (LLMs) for detecting pulp stones under zero-shot conditions remains unclear. Therefore, this study evaluated the diagnostic performance and response consistency of multimodal LLMs in detecting pulp stones on panoramic radiographs of posterior teeth.