Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Retrieval-Augmented Generation for Large Language Models in Radiology: Another Leap Forward in Board Examination Performance
21
Zitationen
5
Autoren
2024
Jahr
Abstract
Perplexity (Perplexity AI), which uses web-based retrieval-augmented generation (RAG) and prioritizes authoritative sources, with GPT-4 Turbo (OpenAI) outperformed default GPT-4 Turbo on a radiology board–style examination, illustrating the utility of RAG optimization to generate radiology answers.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.