aceAccuracy of Artificial Intelligence based chatbots in reporting jaw lesions from multimodal radiographic images: A cross-sectional study
Objectives The current study aimed to quantify the diagnostic accuracy of commonly utilized chatbots including Gemini, Copilot, Claude, and specialized architectures like Manus in the detection and differential diagnosis of various jaw lesions, while concurrently evaluating the clinical safety and fidelity of the information they provide. Materials and methods Cone beam computed tomography (CBCT) dataset from 97 patients presented with jaw lesions were collected and anonymized. Panoramic 2D views were reconstruc…
Chatbot accuracy for jaw lesion reports varied significantly by model and input type, with 3D input improving results for one system (Manus).
Copilot and Claude produced the least accurate reports for jaw lesions, highlighting significant discrepancies in diagnostic accuracy across chatbots.
Evidence
- Peer-reviewedDentomaxillofacial Radiology2026-08-04
How should this claim be treated?
Truvace Impact Record TRV-2026-0653, v2: “Accuracy of Artificial Intelligence based chatbots in reporting jaw lesions from multimodal radiographic images: A cross-sectional study.” Truvace, 2026-10-26. /record/TRV-2026-0653 (accessed at citation time). sha256 4c8bc672f2e90d9a…
Calibration history
Every change to this record since certification, in the open.
Direction audit: the gain line described the method, not an outcome; restated as the finding.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0653 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.