Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Digital guides in eye care: Comparing AI model accuracy and reliability
0
Zitationen
2
Autoren
2026
Jahr
Abstract
LLMs can generate useful responses for patient education in ophthalmology, but performance varies by model and subspecialty. Within this 50-question, text-only expert-rating framework, Gemini 2.0 Pro and ChatGPT o3 Mini High provided relatively higher accuracy and reliability in most areas, whereas LLaMA 3.1 405B lagged. Larger and clinically integrated evaluations, including direct assessment of patient understanding and behavior, are needed to define their safe use in practice.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.545 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.436 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.935 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 · 5.589 Zit.