ReviewFrontiers in medicine2026
Application of artificial intelligence in medical education: Saudi Arabian perspective.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Artificial intelligence (AI) is increasingly reshaping medical education by enhancing learning efficiency, personalization, and assessment. In Saudi Arabia, national investments in digital health and education under Vision 2030 have created a favorable environment for AI integration. However, the adoption of AI in medical education must be carefully balanced to preserve essential humanistic elements such as empathy, mentorship, and interpersonal communication. Methods: This narrative review examines the current state of AI applications in medical education in Saudi Arabia. A structured literature search was conducted across peer-reviewed databases and relevant gray literature to identify studies published in English or Arabic within the past 5 years. Data were extracted, critically appraised, and synthesized narratively to highlight key applications, benefits, challenges, and future directions. Results: AI applications in Saudi Arabian medical education include adaptive learning platforms, simulation-based training, virtual and augmented reality, intelligent tutoring systems, and AI-supported assessment tools. These approaches have been associated with improved learner engagement, knowledge retention, and personalized educational experiences among medical students, residents, and trainees. Despite these advances, formal integration of AI-related competencies-such as data literacy, algorithm interpretation, and ethical AI use-remains limited within current curricula. Conclusion: Artificial intelligence has significant potential to transform medical education in Saudi Arabia and support the development of a future-ready healthcare workforce. While national initiatives and investments have accelerated AI adoption, further research, faculty development, ethical governance, and curriculum reform are required to ensure responsible, effective, and human-centered implementation.
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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.