Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating large language model-generated brain MRI protocols: performance of GPT4o, o3-mini, DeepSeek-R1 and Qwen2.5-72B
1
Zitationen
16
Autoren
2025
Jahr
Abstract
QuestionBrain MRI protocoling is a time-consuming, non-interpretative task, exacerbating radiologist workload. Findingso3-mini demonstrated superior brain MRI protocoling performance. All models showed notable improvements when augmented with local standard protocols. Clinical relevanceMRI protocoling is a time-intensive, non-interpretative task that adds to radiologist workload; large language models offer potential for (semi-)automation of this process.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 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 · 5.428 Zit.