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Health·P Space·Evidence-backed problem·Published 2026-09-22

Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians

Abstract: 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…

TRV-2026-1166Peer-reviewedPermanent record — cite & verify
Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians

Hospital Universitari Doctor Peset, València 02 by 19Tarrestnom65. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

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.

Results AI-Gemini achieved 97.1% accuracy for lesion detection and malignancy classification, with sensitivity 98.5% and specificity 96.2% for malignancy, and 88.0% accuracy for precise histologic diagnosis. The head and neck surgeon achieved the highest accuracy for precise diagnosis (97.7%).

Main points
  • 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.
Problem

Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians: Urgency assignment was correct in 70% of cases (κ = 0.716), with a conservative tendency to overestimate risk.

The rundown

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).

Sources

Reader signal

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The debate