Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Critical evaluation of large language models for human cross-sectional anatomy identification: implications for collaborative intelligence
0
Zitationen
5
Autoren
2026
Jahr
Abstract
Abstract Objective Rapid advances in artificial intelligence have increased interest in using large language models (LLMs) for medical education and clinical applications. This exploratory study evaluated the ability of three multimodal LLMs, ChatGPT 5, Gemini 2.5 Flash, and Grok 4, to identify anatomical structures in cross-sectional images of the upper and lower limbs. Results Twenty cross-sectional images, each highlighting a single anatomical structure, were presented to the models with standardized prompts specifying the anatomical region. Accuracy was scored for each model. ChatGPT 5 correctly identified 9 of 20 structures (45%, 95% CI: 23.1–68.5%), Gemini 2.5 Flash 5 of 20 (25%, 95% CI: 8.7–49.1%), and Grok 4 4 of 20 (20%, 95% CI: 5.7–43.7%). A qualitative error analysis revealed common misclassification patterns. These results indicate modest accuracy under the tested conditions and highlight areas for model improvement.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.527 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.419 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.909 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.578 Zit.