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

Year: 2026

Type: article

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.

Source: Journal of Engineering and Science in Medical Diagnostics and Therapy

DOI: 10.1115/1.4071512

Authors: Xiaoying Fang, Mingxing Lu

Institution: Henan Vocational College of Nursing

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Quality education, Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7141570345