Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Accuracy of large language models in answering ophthalmology board-style questions: A meta-analysis
12
Zitationen
3
Autoren
2024
Jahr
Abstract
The overall accuracy of LLMs in answering ophthalmology board-style questions was acceptable but not exceptional, with ChatGPT-4 and Bing Chat being top-performing models. Performance varied significantly based on specific ophthalmology topics tested. Inconsistent performances are of concern, highlighting the need for future studies to include ophthalmology board-style questions with images to more comprehensively examine the competency of LLMs.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.357 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.221 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.640 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 · 5.482 Zit.