Assessing multimodal large language models for localizing dental implant fixtures on panoramic radiographs

Abstract

OBJECTIVES: To assess whether general-purpose multimodal large language models (LLMs) can localize dental implant fixtures on panoramic radiographs and to quantify false positives on implant fixture-absent images. METHODS: . RESULTS: values were 1.95, 1.04, and 0.92, respectively. Radiopaque restorations markedly reduced specificity. The fixtures detected in all five runs were 1.01 % (GPT-4o), 22.22 % (OpenAI o3), and 25.93 % (GPT-5T). CONCLUSIONS: Reasoning-focused multimodal LLMs outperformed GPT-4o in zero-shot implant fixture localization and reduced false positives, but moderate sensitivity, restoration-driven errors, and run-to-run variability limit autonomous clinical use. CLINICAL SIGNIFICANCE: This benchmark clarifies the current capabilities and limitations of multimodal LLMs for implant-related radiographic workflows.

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