Sycophancy in Large Language Models in Oral and Maxillofacial Radiology: The Impact of Misleading Context on Image- and Text-Based Diagnostic Scenarios

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.