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TruaceTracing the truth around AIWednesday, August 5, 2026
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TRV-2026-0653Certified recordPeer-reviewed

Accuracy 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…

Health · The Trace — both readings · certified 2026-08-05 · v1 · article view · machine-readable

Current reading — gain

Manus architecture using raw 3D CBCT data detected and correctly diagnosed 95% of jaw lesions in 97 patients, outperforming 2D panoramic inputs.

Current reading — problem

Copilot and Claude produced the least accurate reports for jaw lesions, highlighting significant discrepancies in diagnostic accuracy across chatbots.

Evidence

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Truvace Impact Record TRV-2026-0653, v1: “Accuracy of Artificial Intelligence based chatbots in reporting jaw lesions from multimodal radiographic images: A cross-sectional study.” Truvace, 2026-08-05. /record/TRV-2026-0653 (accessed at citation time). sha256 3010787d0715991b…

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