Cultivating practical literacy of machine learning for medical students

Abstract

Machine learning (ML) is rapidly evolving into clinical artificial intelligence (AI) systems that can assist specific medical decisions. However, at present, ML faces the limitation factors such as high heterogeneity of clinical data and difficulty in data analysis for facilitating clinical performance. Future, “intelligent” doctors should dominate innovations in medicine and AI engineering around clinical mission and clinical data. Therefore, in the form of medical student internship or resident training case learning, adding practical training such as estimation of the minimum sample size required for ML and clinical interpretation of advanced characteristics of data is conducive to cultivating medical students with clinical capacity of reasoning to practice of computer-based diagnosis and treatment, and to develope a more efficient intelligent medical decision-making system.

Keywords

• machine learning|medical education|clinical data