aceImage-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians
Objective To evaluate the real-world diagnostic performance of a multimodal large language model (LLM) for image-based assessment of oral mucosal lesions compared with clinicians of varying expertise. Study design Prospective international multicenter diagnostic accuracy study. Setting Twenty university and tertiary head and neck centers in Italy, Belgium, France, Spain, and Israel. Methods We enrolled 350 consecutive patients (320 with oral lesions, 30 with normal mucosa). Clinical photographs and basic epidemi…
The AI chatbot assigned the correct urgency in only 70% of cases and tended to overestimate risk.
Sources
- Peer-reviewedOtolaryngology–Head and Neck Surgery2026-09-21
What do you make of this one?
Truvace Impact Record TRV-2026-1166, v2: “Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians.” Truvace, 2026-11-04. /record/TRV-2026-1166 (accessed on the day you copy it). sha256 98e25e6c47990c1c…
Change history
Every change made to this record since it was first published, and why.
Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
Certified into the record
How to check it yourself, without trusting this page
Download any version from /api/record/TRV-2026-1166 and hash its canonical field yourself, for example with shasum -a 256. You should get its content_hash. Each version’s text also ends with prev:and the hash of the version before it (version 1 points to 64 zeros). If anyone had changed a single character of any version after it was saved, those hashes wouldn’t line up.