ArticleBMC oral health2026
Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundDifferentiating jawbone-destroying malignancy from osteomyelitis remains a major diagnostic challenge in oral and maxillofacial surgery because these entities share overlapping radiologic features but require fundamentally different management strategies. This study evaluated the bi-linguistic diagnostic performance of advanced multimodal large language models (LLMs) in distinguishing these conditions using stepwise multimodal inputs.
methodsIn this retrospective diagnostic accuracy study, 50 patients with histopathologically confirmed malignancy or osteomyelitis of the maxilla or mandible were included. Three multimodal LLMs (ChatGPT, Claude, and Gemini) were assessed using standardized prompts in Korean and English under three imaging conditions: panoramic radiograph only (P), panoramic radiograph plus computed tomography (CT) (P + C), and panoramic radiograph plus CT plus histopathology slide (P + C+B). Diagnostic accuracy, sensitivity, and specificity were evaluated against histopathology as the reference standard using generalized linear mixed models.
resultsOverall diagnostic accuracy increased significantly with additional modalities, from 0.683 (95% CI, 0.542–0.797) under the P condition to 0.776 (95% CI, 0.652–0.865) under P + C, and to 0.978 (95% CI, 0.953–0.990) under P + C+B. Incorporation of histopathology slides markedly increased the odds of a correct diagnosis compared with P and P + C conditions (both p < 0.0001), while CT addition alone showed a nonsignificant trend toward improvement. Under limited imaging conditions, models tended to overdiagnose malignancy, reflecting high sensitivity but low specificity. With full multimodal input, all models achieved balanced diagnostic performance across models and languages.
conclusionNotably, ChatGPT demonstrated higher diagnostic accuracy in the Korean-language condition than in English. Overall, these findings suggest that multimodal LLMs may support diagnostic interpretation by integrating heterogeneous imaging information, highlighting their potential role as adjunctive decision-support tools within existing maxillofacial diagnostic frameworks.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.