Hierarchical Reasoning Gates for Multimodal Large Language Models in Dental Image Interpretation: A Prompt-Level...

Multimodal large language models (MLLMs) have been used to support dental image interpretation, but performance may depend on prompt structure. We proposed Hierarchical Reasoning Gates (HRG), a prompt-level protocol organizing image interpretation into sequential stages with validation before progression. Forty anonymized panoramic radiographs of impacted mandibular molars were analyzed using three prompt architectures: P1, zero-shot natural language; P2, a five-step structured workflow followed by a final internal-validation checkpoint; and P3, HRG with 10 macro-stages and 35 procedural control blocks. Outputs were compared with expert radiology reference reports using five predefined variables. Mean Total Score was 3.52 ± 0.85, 3.58 ± 0.71, and 4.35 ± 0.74 for P1, P2, and P3, respectively; Full Accuracy was 10.0%, 5.0%, and 50.0%. Reports with errors in three or more variables occurred in 12.5%, 7.5%, and 0%, respectively. Only Apparent Anatomical Relationship differed significantly across prompts after Holm adjustment. Test-retest repeatability was high for several variables but lower for Apparent Anatomical Relationship in P3. HRG was associated with higher image-interpretation performance and fewer complex error patterns, although higher performance did not uniformly translate into greater repeatability. These findings support gated prompt design for controlling dependent steps in MLLM-based image analysis, while clinical use still requires expert supervision.