Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Perceptions of large language models in medical education and clinical practice among pediatric emergency physicians in Saudi Arabia: a multiregional cross-sectional study
3
Zitationen
6
Autoren
2025
Jahr
Abstract
Background: Artificial intelligence (AI) is reshaping healthcare delivery and education, but little is known about its perceived value among pediatric emergency medicine (PEM) physicians in Saudi Arabia. This study aimed to assess the perceptions and experiences of PEM physicians in Saudi Arabia toward the use of AI, particularly ChatGPT, in clinical practice and medical education. Methods: A cross-sectional, web-based survey was conducted among 100 PEM physicians across various regions of Saudi Arabia. The questionnaire explored demographics, AI experience, perceived benefits and limitations, and the evaluation of ChatGPT-generated clinical and educational content. Results: Most participants (96%) believed that AI tools, such as ChatGPT, would play a significant role in the future of PEM. A high agreement was observed regarding AI's usefulness in medical education (91%) and clinical practice, particularly in differential diagnosis (77%) and documentation (78%). The ChatGPT-generated responses to a clinical scenario (croup) were rated highly for validity, reasoning, and educational value. However, 66% of them still preferred traditional textbooks for complex topics. The key concerns included accuracy (83%), patient safety (56%), and lack of regulatory guidance (52%). Conclusion: Saudi PEM physicians show a strong interest in integrating AI tools, such as ChatGPT, into clinical and educational workflows. Although optimism is high, concerns about safety, ethics, and oversight highlight the need for regulatory frameworks and structured implementation strategies.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.773 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.682 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.242 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.