ArticleThe Laryngoscope2026
Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.
Article in The Laryngoscope, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Evaluating Injection Laryngoplasty Skills Using a Foundation Model: A Feasibility Study.The Laryngoscope · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the construct validity of a commercially available multimodal foundation model (Google Gemini 2.5 Pro) in assessing simulated injection laryngoplasty.
methodsThirty video recordings of simulated injection laryngoplasty procedures were stratified by operator experience (10 novice, 10 intermediate, and 10 expert participants). Videos were evaluated by Gemini 2.5 Pro using two prompt engineering strategies: zero-shot (rubric-based, no examples) and few-shot (rubric plus examples). Performance was compared against operator training level (ground truth). Model reliability and stability were assessed through 90 repeated inference trials.
resultsUnder a zero-shot strategy, the model failed to discriminate between skill levels (Spearman's ρ = 0.12, p = 0.52). Conversely, few-shot prompting demonstrated a strong, positive correlation with operator experience (Spearman's ρ = 0.66, p = 0.0002) and successfully stratified skill levels (Kruskal-Wallis H = 12.4, p = 0.002). Pairwise analysis confirmed the few-shot model significantly differentiated experts from both novices (p = 0.002) and intermediates (p = 0.026). Additionally, few-shot prompting significantly improved precision, reducing mean absolute error by nearly half (0.74-0.41, p = 0.04). Reliability analysis revealed 100% ordinal consistency (75.6% exact match stability), indicating the model varied under identical conditions, but did not commit any gross classification errors.
conclusionGeneral-purpose multimodal models lack the intrinsic surgical judgment necessary to assess procedural skill. However, resource-efficient few-shot prompting successfully calibrates the model to distinguish expert from trainee performance. While promising as a scalable assessment tool, current models exhibit inherent variability that requires mitigation, such as averaging repeat model evaluations. LEVEL OF EVIDENCE: N/A.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.