With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data’s “datathons”, the authors advocate for a dual-focused approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.

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关于人工智能，医学生实际上需要了解什么？


随着人工智能 (AI) 的新兴创新有望对医疗实践产生重大影响，人们对当前和未来医生进行该技术培训的兴趣与日俱增。随之而来的问题是，医学生到底应该学什么？虽然人工智能临床应用的能力与任何其他新技术的能力大致相似，但在可解释性、健康公平性和数据安全性方面存在至关重要的质量差异。借鉴多伦多大学医学院和麻省理工学院关键数据“数据马拉松”的经验，作者提倡采用双重重点方法：将强大的以数据科学为重点的补充内容与基线健康研究课程和课外项目相结合，以培养该领域的领导力。