Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence in Craniosynostosis Surgery: A Comparison of Large Language Models in Answering Perioperative Care Questions
0
Zitationen
7
Autoren
2026
Jahr
Abstract
LLMs vary significantly in readability and information quality. Google Gemini offered the most trustworthy content, whereas DeepSeek was most accessible. No single model excelled across all dimensions, suggesting that clinicians should guide caregivers toward LLMs best suited to their literacy level. Generative AI holds promise for augmenting patient education in craniosynostosis care. However, it should be used alongside clinician input to ensure clarity, accuracy, and relevance.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.