OrthoAgent: A knowledge-enhanced multi-agent framework for multimodal orthodontic diagnosis and treatment planning

Introduction: Automated orthodontic diagnosis requires integration of multimodal geometric evidence with clinically grounded treatment reasoning. Existing multimodal models often lack the spatial precision required for orthodontic phenotyping, whereas specialized deep-learning tools usually remain disconnected from treatment planning. Materials and methods: We developed OrthoAgent, a training-free hierarchical multi-agent framework for multimodal orthodontic diagnosis and treatment planning. Dedicated perception agents extract structured findings from 2D images, cone-beam computed tomography, and intraoral scans. These findings are integrated into a patient-specific diagnostic baseline, while a retrieval-augmented generation module grounds treatment reasoning in a curated dental knowledge corpus without modifying perception-derived patient facts. OrthoAgent was retrospectively evaluated on 69 real-world orthodontic cases using objective diagnostic tasks, blinded expert review, clinician-rated reusability, a time-tracked human–AI collaboration study, and paired ablation comparisons. Results and discussion: OrthoAgent achieved a mean accuracy of 90.5% across 12 classification tasks and low error across 17 continuous measurements. Generated reports received an overall expert score of 3.43/5, with the highest score for safety (4.19/5). Clinician-rated reusability corresponded to an estimated 46.8% reduction in editing burden, and direct workflow timing showed a 41.74% reduction in end-to-end completion time. In 20 paired comparisons, retrieval-grounded reports were numerically preferred over non-retrieval reports in 14 cases. Conclusion: OrthoAgent provides a proof-of-concept framework for structured, evidence-informed orthodontic decision support under clinician oversight. Larger prospective, multi-center studies are needed to establish robustness and generalizability.