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Diagnosing jawbone lesions in oral and maxillofacial radiology remains challenging due to overlapping radiological features and the need for integrated clinical reasoning. This study aimed to develop and validate JADE-Plus, a novel multimodal, agent-controlled retrieval-augmented generation (RAG) framework for diagnostic decision support in jawbone lesion assessment. JADE-Plus was implemented as a cloud-based, tablet-optimized system integrating a vision-language model (VLM) for panoramic radiograph analysis, a knowledge-grounded RAG module, and an agentic verification loop for diagnostic fusion and re-ranking. The system was evaluated using 40 representative jawbone lesion cases and compared with JADE, GPT-5.4, GPT-5.4 VLM, and ORAD. Performance was assessed using Top-1 and Top-3 accuracy, ablation and statistical analyses, intra-model stability, and response time. JADE-Plus achieved the highest diagnostic performance, with a Top-1 accuracy of 90% (36/40; 95% CI 76-97%) and a Top-3 accuracy of 100%, with no missed diagnoses. Cochran's Q test demonstrated significant differences among models for both Top-3 correctness (Q = 25.66, p < 0.001) and Top-1 correctness (Q = 27.55, p < 0.001). Post-hoc McNemar tests with the Benjamini-Hochberg correction showed that JADE-Plus significantly outperformed other models, particularly under the stricter Top-1 evaluation. Ablation analysis showed that the agentic verification stage improved Top-1 and Top-3 accuracy by 15 and 8 percentage points, respectively. JADE-Plus achieved the highest reproducibility (mean Jaccard similarity 0.97 ± 0.12) while maintaining a mean response time of 33 ± 1.5 s per case. JADE-Plus demonstrated superior diagnostic accuracy, stability, and reproducibility compared with baseline systems, supporting multimodal agentic RAG frameworks for jawbone lesion diagnosis.
Saadi et al. (Mon,) studied this question.