ReviewBMC medical education2025
Surgical education reimagined: the convergence of learning theories and artificial intelligence.
Review in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- Sensorized Vascular High-Fidelity Physical Simulator for Robot-Assisted Surgery Training: A Multisite Pilot Evaluation.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe integration of artificial intelligence (AI) into surgical education is transforming the way surgical skills and knowledge are developed. This article examines how AI aligns with key educational theories-behaviourism, cognitivism, constructivism, humanism, and connectivism-to enhance learning through personalised simulations, adaptive feedback, and networked platforms. MATERIALS AND
methodsA review of literature and theoretical frameworks was conducted to analyse AI's applications in surgical training. Key features include AI-driven tools for structured feedback, cognitive optimisation, experiential learning, individual growth, and collaboration through interconnected networks. The article also identifies ethical challenges, including data privacy, algorithmic bias, and equitable access.
resultsAI has the potential to revolutionise surgical education by fostering critical thinking, improving training outcomes, and expanding access to learning resources. However, risks such as over-reliance on automation, loss of hands-on experience, and superficial AI use ("AI theatre") highlight the need for thoughtful and ethical implementation.
conclusionWith a balanced and collaborative approach among educators, technologists, and healthcare professionals, AI can create dynamic, learner-centred environments. By addressing challenges, AI can support the development of skilled, compassionate surgeons equipped to navigate the complexities of modern medical practice.
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.