ArticleFrontiers in public health2025
Perceptions of large language models in medical education and clinical practice among pediatric emergency physicians in Saudi Arabia: a multiregional cross-sectional study.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Factors influencing large language model adoption among dental students: a cross-sectional study.Scientific reports · 2026Article
- Physicians' Knowledge, Perceptions and Use of Large Language Models in Clinical Practice:Sultan Qaboos University medical journal · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.