Retrieval-augmented generative AI enhances clinical reasoning in odontogenic sinusitis versus maxillary sinus mucositis.

PURPOSE: This exploratory pilot study evaluated whether combining structured clinical information, medical imaging, and literature-derived knowledge could enhance the diagnostic reasoning and output quality of a large language model in distinguishing odontogenic sinusitis from maxillary sinus mucositis. METHODS: Six complex clinical cases were constructed with nasal endoscopy, computed tomography findings, and clinical vignettes. ChatGPT-4.0 was prompted using four strategies: (1) clinical text only, (2) text with medical imaging, (3) text with structured literature excerpts, and (4) text with both imaging and literature input. Seven blinded expert reviewers rated 168 responses across diagnostic accuracy, clinical reasoning, safety, and overall usefulness using a five-point scale. Statistical comparisons and inter-rater reliability were assessed. RESULTS: All prompting strategies produced clinically safe outputs with minimal hallucinations or unsafe recommendations. Combining structured literature and imaging with clinical text significantly improved clinical reasoning scores (F(3,123) = 4.32, p = 0.0058), ranking first or second in six of seven evaluation domains. No significant differences were observed in diagnostic accuracy or safety across strategies. Inter-rater reliability was substantial (kappa = 0.7). CONCLUSION: Providing structured evidence and imaging appeared to enhance the clinical reasoning quality of large language models in this small, simulated dataset, without compromising diagnostic accuracy or safety. These preliminary findings suggest that structured multimodal prompting may help improve the interpretability and reliability of artificial intelligence tools for supporting diagnostic reasoning in sinus-related diseases, though larger prospective validation studies are needed before clinical implementation.