Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Prompting is All You Need: How to Make LLMs More Helpful for Clinical Decision Support
0
Zitationen
2
Autoren
2026
Jahr
Abstract
Structured prompting substantially enhances LLM performance for acute stroke thrombolysis CDS. Notably, some models, including the proprietary GPT-4o and o3, and the open-source reasoning model r1-1776, achieved excellent safety and adherence with structured prompts. For clinical deployment of any LLM, structured prompts are crucial, and vigilant human oversight remains essential.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.316 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.177 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.575 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 · 5.468 Zit.