Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Valutazione del ragionamento clinico dei reasoning large language models su casi clinici complessi
0
Zitationen
11
Autoren
2025
Jahr
Abstract
Large language models (LLMs) show promise in explicit reasoning for complex medical fields like psychiatry. This study assessed the clinical validity of Gemini's chain-of-thought (CoT) reasoning in 10 complex psychiatric cases, evaluated by specialists using six metrics. Results indicate high performance (average score ≥4.26/5), especially in step sufficiency and factual accuracy, suggesting that CoT reasoning by LLMs can support transparent and detailed clinical decision-making.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.250 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.109 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.482 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 · 5.434 Zit.