Evaluating Generative AI (Microsoft Copilot) as an Adjunctive Decision-Support System in Oral and Maxillofacial Radiology: A Retrospective Study

Abstract

Objectives: To assess the utility of Microsoft Copilot, a generative AI tool, in providing meaningful differential diagnosis and management strategies comparable with those generated by a board-certified radiologist using cone beam computed tomography (CBCT) studies in maxillofacial disease and thus assess its potential utility to help with the initial provisional diagnostic process. Study Design: A pilot project designed as a single-center, retrospective study using a convenient sample was conducted. De-identified data collected from patient charts in a consistent format was fed to Microsoft 365 Copilot (MCP) to generate a list of meaningful differential diagnosis (DD) and management protocols. Scores ranging of 0–3 were given for 0–3 matches in DD and management protocols, respectively. Results: Proportional analysis showed that the radiologist and Copilot agreed on the DD in 75.2% of cases and 94.6% of cases in management protocols. For biopsy recommendations, the radiologist and Copilot advised biopsy in 33 (89.2%) cases while they did not recommend biopsy in 23 (41.8%) cases. Conclusions: Generative AI platforms at this point may have value in generating DD and management protocols based on maxillofacial CBCT findings. However, the radiologist’s judgement based on clinical context, feature recognition, and critical analysis seemed to outperform MCP. Larger studies with statistical validation are warranted.

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