The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-1166
version: 2
kind: revised
reason: Editorial pass: sharper headline; gain/problem restated as concrete outcomes.
timestamp: 2026-11-04T09:50:24.177075Z
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: The AI chatbot assigned the correct urgency in only 70% of cases and tended to overestimate risk.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: The AI chatbot assigned the correct urgency in only 70% of cases and tended 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
prev: d25b8eb0703f13d7f89881cebb2a5499e1e16a1bf1296b08a47cd82b1603ba85

SHA-256 hash

98e25e6c47990c1c8317318dfe515c586e336f11c64f23da6ef8ca160d13aba3

Previous version’s hash

d25b8eb0703f13d7f89881cebb2a5499e1e16a1bf1296b08a47cd82b1603ba85