Artificial Intelligence-Assisted Career Planning Model Construction for Clinical Medicine Students

Abstract

Abstract. To address the difficulty in dynamically coupling student competencies with medical job requirements in clinical medical education, this paper applies a career planning model based on a dynamic coupling mechanism. First, the analytic hierarchy process (AHP)-entropy weight method is used to quantify student clinical competencies. This method, combined with the LSTM (long short-term memory) algorithm, predicts future departmental talent needs. A weighted Euclidean distance and cosine similarity fusion algorithm is designed to dynamically match competencies with positions. Collaborative filtering and knowledge graph techniques are further incorporated to generate personalized career path recommendations. Competency assessment and recommended paths are dynamically updated through an online learning mechanism. Finally, SHAP (Shapley additive explanations) interpretability analysis is integrated to visualize the contribution of each competency dimension to the recommended results. Experimental results demonstrate that the proposed model achieves high competency assessment accuracy (average 0.855) and job prediction accuracy (average 7.47%). The overall adoption intention for the top-1 recommended path is as high as 74.2%, effectively improving the scientificity, precision, and practicality of medical students' career planning.