ArticleInternational journal of surgery (London, England)2025
Multimodal large language models address clinical queries in laryngeal cancer surgery: a comparative evaluation of image interpretation across different models.
Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Laryngeal Anatomy and Morphometry: Foundations for Interdisciplinary Collaboration and Personalized Management of Laryngeal Pathology.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- Review
- New frontiers in radiologic interpretation: evaluating the effectiveness of large language models in pneumothorax diagnosis.PloS one · 2025Article
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Authors and funding
5 authors.
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Abstract
BACKGROUND AND
objectivesRecent advances in multimodal large language models (MLLMs) have shown promise in medical image interpretation, yet their utility in surgical contexts remains unexplored. This study evaluates six MLLMs' performance in interpreting diverse imaging modalities for laryngeal cancer surgery.
methodsWe analyzed 169 images (X-rays, CT scans, laryngoscopy, and pathology findings) from 50 patients using six state-of-the-art MLLMs. Model performance was assessed across 1084 clinically relevant questions by two independent physicians.
resultsClaude 3.5 Sonnet achieves the highest accuracy (79.43%, 95% CI: 77.02%-81.84%). Performance varied significantly across imaging modalities and between commercial and open-source models, with a 19-percentage point gap between the best commercial and open-source solutions.
conclusionAdvanced MLLMs show promising potential as clinical decision support tools in laryngeal cancer surgery, while performance variations suggest the need for specialized model development and clinical workflow integration. Future research should focus on developing specialized MLLMs trained on large-scale multi-center laryngeal cancer datasets.
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