ReviewFrontiers in public health2026
The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education.
Review in Frontiers in public health, 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
Background: The use of artificial intelligence (AI) and other data-driven technologies is changing how medical education and clinical teaching are conducted. The traditional model of learning will not allow us to keep up with the sheer amount of new knowledge being produced, the move to competency-based education, or the need for learning that can be scaled and tailored to fit individuals. To examine how AI is being used in clinical education and evaluate its impact on teaching, learning, assessment, and quality assurance. Methods: A review of the literature was conducted to examine the applications of AI in clinical education, including adaptive learning, intelligent tutoring, virtual patients, simulation, automated assessment, and data-driven quality assurance. Results: AI has been shown to enhance the engagement of learners, create flexible learning opportunities, provide timely feedback for information on a learner's progress, and provide ongoing objective assessment of clinical competency. The use of learning analytics and predictive modeling enables institutions to monitor the progression of the learner, identify learners at risk, optimize the design of their curriculum, and make evidence-based academic decisions. Future innovations in medical training environments could be possible through the use of digital twins, immersive simulation, and explainable AI. However, there remain significant challenges to the widespread implementation of these technologies, such as bias in algorithms, lack of transparency, issues related to data privacy, unregulated use, and the limitations of current systems. Research to date has demonstrated great variation across studies and has not provided longitudinal data to support any conclusions about the long-term impact of using AI tools. Conclusions: Despite the great advantages offered by using AI-based technologies in clinical education, Successful integration will require robust governance frameworks, faculty development, equitable access, rigorous validation, and continuous evaluation to ensure ethical, effective, and sustainable 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.