ReviewFrontiers in medicine2026
Artificial intelligence assisted simulation and surgical video analytics for ophthalmic surgery training and competence development.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Application effect and teaching evaluation of case-based learning combined with ChatGPT in ophthalmology clinical teaching.Frontiers in medicine · 2026Article
- Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.Frontiers in public health · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Based on the Dreyfus model of skill acquisition, this article classifies the professional development of ophthalmologists into four stages: novice, advanced beginner, competent, and expert. In this review, artificial intelligence (AI) is operationally defined as data-driven algorithms that enable prediction, perception, and objective assessment from multimodal surgical data. We distinguish AI methods from immersive hardware, such as virtual reality (VR), which serves as a training interface that may or may not incorporate AI-driven assessment and feedback. Accordingly, this manuscript focuses on AI-enabled simulation, computer-vision-based surgical video understanding, and registry/EHR-driven clinical practice and training continuum. At the novice stage, AI-enabled assessment within VR simulation helps trainees form muscle memory and standardized operating habits. This is achieved through haptic-enabled modules and objective performance metrics. For advanced beginners, computer-vision models and attention-visualization techniques support surgical workflow understanding and structured debriefing, assisting trainees in building surgical logic and spatial cognition. When doctors reach the competent stage, AI uses large-scale clinical data to estimate complication risk and support scenario-based crisis training, strengthening complication management and non-technical skills. At the expert stage, AI-assisted surgical video analytics can benchmark technique patterns and surface potential blind spots, facilitating continuous calibration and knowledge sharing. Overall, the evidence to date suggests that AI is best positioned as an assistive tool to augment human learning and decision-making. However, generalizability, interpretability, data governance, and medicolegal accountability remain key barriers to safe and scalable deployment.
Indexed as
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What Socratic 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 Socratic graph.