Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Can Artificial Intelligence Make the Cut? Dissecting Large Language Model’s Surgical Exam Performance
1
Zitationen
2
Autoren
2024
Jahr
Abstract
We read the article by Ostrovsky et al1 with great interest. This article provides valuable insights into the capabilities and limitations of large language models (LLMs) in the context of medical education. The authors have meticulously evaluated the accuracy and reliability of 5 different LLMs in answering surgery clerkship examination questions, presenting a nuanced view of the potential roles these tools might play in enhancing surgical education.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.100 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.466 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 · 5.429 Zit.