Diagnostic Accuracy of Large Language Models for Peri-Implant Marginal Bone Loss on Panoramic Radiographs

Dental implants are a routine treatment for missing teeth, but the number of implants affected by peri-implantitis, a progressive inflammatory disease of the tissues around implants, is increasing. The main radiographic sign of peri-implantitis is marginal bone loss around the implant. Early detection and correct grading of this bone loss in proportion to implant length are important for treatment planning and prognosis. Panoramic radiographs are widely used for general screening in dental practice, but judging bone loss on them is subjective, time-consuming and depends on the clinician's experience. Multimodal large language models (vision-language models) are increasingly able to interpret images, yet their ability to evaluate peri-implant bone loss on panoramic radiographs has not been tested. This single-center, retrospective, observational diagnostic accuracy study will evaluate how accurately five publicly available large language models (ChatGPT, Gemini, Claude, DeepSeek and Grok) detect and grade peri-implant marginal bone loss on anonymized, cropped panoramic radiographs of functionally loaded dental implants, compared with a reference standard established by three blinded clinicians (two periodontologists and one oral and maxillofacial radiologist). Bone loss will be graded with the Froum and Rosen (2012) classification: Score 0 (no visible bone loss), Score 1 (\<25% of implant length), Score 2 (25-50%) and Score 3 (\>50%). Approximately 784 implant sites (392 with and 392 without marginal bone loss) from existing panoramic radiographs in the hospital archive will be included. No new radiographs will be taken, no patient will be contacted and no treatment will be given; informed consent has been waived by the ethics committee. All images will be fully anonymized before evaluation.