Utku Cem Hasırcı

Background/Objectives: Large language models (LLMs) are increasingly used in clinical decision-support, yet their sycophantic behavior in response to user-provided misleading contextual cues remains underexplored in image-based disciplines such as oral and maxillofacial radiology. This study compared sycophantic behavior across five LLMs using image-based and text-based diagnostic scenarios in oral and maxillofacial radiology. Methods: ChatGPT-5.3, Google Gemini 3, Grok 4, Claude Sonnet 4.6, and DeepSeek V3.2 were evaluated on 200 scenarios (100 image-based and 100 text-based) under three prompt conditions: neutral, misleading-cue, and authoritative-cue. The resulting 3000 responses were analyzed using generalized estimating equations (GEE). Results: Diagnostic accuracy ranged from 53.5% to 65.0% under the neutral condition and declined to 28.5–44.0% under the authoritative-cue condition. The authoritative-cue condition had significantly higher odds of sycophancy than the misleading-cue condition (adjusted odds ratio [AOR] = 2.92; 95% confidence interval [CI], 2.52–3.37). Modality had no significant main effect on diagnostic accuracy or sycophancy outcomes; however, the Model × Modality interaction was significant for the overall sycophancy rate (p = 0.014). Under the authoritative-cue condition, self-reported confidence did not differ significantly between correct and incorrect responses (p = 0.678). Conclusions: LLMs are susceptible to misleading contextual cues in oral and maxillofacial radiology diagnostic scenarios, particularly when misinformation is presented as an authoritative cue. The dissociation between self-reported confidence and accuracy indicates that self-reported confidence scores alone are unreliable indicators of diagnostic performance. LLMs should therefore serve as supportive tools requiring expert verification rather than as independent decision-makers in clinical practice.

Background/Objectives: This study aimed to compare the diagnostic performance of multimodal (image-capable) large language models (LLMs) and oral and maxillofacial radiologists in detecting nine predefined incidental findings on panoramic radiographs, using a cone-beam computed tomography (CBCT)-derived reference standard. Methods: This retrospective diagnostic performance study included 500 purposively assembled, finding-enriched panoramic radiographs paired with CBCT images. CBCT images were evaluated by three radiologists to establish the reference standard. Two experts and three LLMs, the latter accessed through their consumer web interfaces, independently assessed the presence or absence of the nine findings; 4500 finding-level decisions were analyzed for each reader. Sensitivity, specificity, accuracy, and error rates were calculated. Within-patient clustering was accounted for using generalized estimating equations and a cluster bootstrap procedure with 5000 resamples. Finding-specific comparisons used Cochran’s Q test with Benjamini–Hochberg correction. Results: Agreement between the two experts was very good (κ = 0.86). Expert sensitivity, specificity, and accuracy ranged from 89.5 to 91.6%, 92.0–93.0%, and 91.8–92.9%, respectively, compared with 75.1–83.1%, 88.0–90.0%, and 86.8–89.0% for the LLMs. The overall reader effect was significant for all three performance metrics (all p < 0.001). In the exploratory high-risk group, expert sensitivity ranged from 90.9 to 93.2% versus 62.9–78.0% for the LLMs. After correction for multiple comparisons, the difference between readers remained significant only for carotid artery calcification (q < 0.001) and extensive maxillary sinus pathology (q = 0.005). Conclusions: Under the consumer-interface conditions and access period tested, the LLMs performed below the experts, particularly for high-risk findings, and should not be used independently to evaluate panoramic radiographs. Because the dataset was finding-enriched and single-center, the absolute estimates cannot be transferred directly to routine clinical populations, and any future role for these models, more plausibly as an expert-supervised adjunct or screening aid than as a replacement for expert interpretation, remains to be tested prospectively.

The nasopalatine canal (NPC) shows considerable morphological variability and may be related to adjacent anatomical structures. The aim of this study was t