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TRUVACE RECORD VERSION
record: TRV-2026-1166
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-22T06:53:35.479864Z
status: published
lens: p_space
sector: health
headline: Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
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%).
limitation: 
tag: Evidence-backed problem
key_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.
rundown: 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).
sources:
- peer_reviewed | Otolaryngology–Head and Neck Surgery | https://doi.org/10.1002/ohn.70437 | 2026-09-21
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