Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis
54
Zitationen
10
Autoren
2024
Jahr
Abstract
MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 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 · 5.423 Zit.