Enhancing clinical training evaluation: leveraging artificial intelligence algorithms for effective online practicum assessment

Abstract

This 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. A variety of artificial intelligence algorithms are used. The 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. When 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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