Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating the Ability of Artificial Intelligence to Address Nuanced Cardiology Subspecialty Questions: ChatGPT and CathSAP
1
Zitationen
8
Autoren
2025
Jahr
Abstract
The study demonstrates ChatGPT's potential for learning and adapting to medical examination scenarios, with a notable enhancement in performance after exposure to educational materials. However, limitations such as the model's inability to process certain visual materials and potential biases in AI models warrant further consideration. These findings underscore the need for continued research to optimize the use of AI in medical education and assessment.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.445 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.325 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.761 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.530 Zit.