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March 29, 2026Journal of Engineering and Science in Medical Diagnostics and TherapyOpen Access

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

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Authors

XFXiaoying FangMLMingXing Lu

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Overview

This model predicts career paths and competencies for clinical medical students, suggesting improvements in job placement accuracy.

Key Points

  • The research aims to create a dynamic career planning model that aligns student competencies with medical job requirements.
  • Utilized the Analytic Hierarchy Process (AHP)-entropy weight method for competency quantification.
  • Employed the Long Short-Term Memory (LSTM) algorithm for future departmental talent predictions.
  • Designed a fusion algorithm using weighted Euclidean distance and cosine similarity for competency-job matching.
  • Incorporated collaborative filtering and knowledge graph techniques for personalized career recommendations.
  • Implemented SHAP interpretability analysis to visualize competency contributions.
  • Achieved an average competency assessment accuracy of 0.855.
  • Secured a job prediction accuracy of 7.47% on average.
  • Noted a 74.2% adoption intention for the top recommended career path.

Cite This Study

Fang et al. (2026) studied this question.

synapsesocial.com/papers/69c8c371de0f0f753b39e425https://doi.org/10.1115/1.4071512
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