ArticleBMC medical education2026
Enhancing clinical training evaluation: leveraging artificial intelligence algorithms for effective online practicum assessment.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in the design of highly compatible knee prostheses: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Elucidating the transformative role of large language models in advancing anesthesiology education.The Journal of international medical research · 2026Review
- Development and interpretability of a machine learning model for postoperative hypothermia prediction in placenta previa Cesarean section: A retrospective cohort study.The Journal of international medical research · 2026Article
- Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis framework shall be founded upon the amalgamation of multiple AI algorithms and course data, with the overarching objective of furnishing medical students with real-time feedback and personalized learning support.
methodsA variety of artificial intelligence algorithms are used.
resultsThe outcomes derived from the test group analysis of AI algorithms for predicting satisfaction with online clinical apprenticeships were as follows: in terms of accuracy, all the algorithms achieved the accuracy of over 70% except for LGBM and GradientBoosting; in terms of precision, the top five best algorithms were GradientBoosting (0.917), LGBM (0.880), CNN (0.865), LinearSVC (0.851), and CatBoost (0.833); in terms of recall, the top three best algorithms were gnb (0.415), PBNN (0.410), and RNN (0.410); in terms of F1 score, the top four best algorithms were PBNN (0.536), NeuralDecisionTree (0.520), Gnb (0.514), and NeuralDecisionForest (0.509) ; and in terms of AUC, most of the algorithms displayed high performance levels except for RNN. Our web-based platform has been successfully implemented through the utilization of the LinearSVC algorithm. This system is conveniently accessible via a designated web page ( https://zhouchengmao-streamlit-app-5-lsvc-a-st-app-lsvc-apsoocpc-gxze7n.streamlit.app/ ), where users may engage with its functionalities.
conclusionsWhen considering the results of both the training and test groups collectively, it was evident that Linear SVC demonstrated favorable performance across multiple evaluation metrics. Therefore, it could be regarded as one of the best-performing algorithms.
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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.