Machine learning is on the rise. According to Scopus (www2.scopus.com), the number of publications in medicine with machine learning in the title, abstract, or as a keyword during 2016 to 2018 increased from 1658 to 3904. In psychiatry, applications of machine learning are proposed to improve the accuracy of diagnosis and prognosis and determine treatment choice. At the same time, much of this research has given insufficient attention to high-quality methods, clinical applications, and ethical aspects. This is compounded by poor reporting of performative measures and misleading claims about the high accuracy of such approaches. In this issue of JAMA Psychiatry, the article by Gradus and colleagues¹ raises important questions about the place of machine learning in research and practice.

机器学习正在兴起。根据Scopus（www2.scopus.com）的统计，2016年至2018年期间，以机器学习为标题，摘要或关键词的医学出版物从1658种增加到3904种。在精神病学中，机器学习的应用被提议用于提高诊断和预后的准确性，并确定治疗方案。同时，许多研究对高质量的方法，临床应用和伦理方面都没有给予足够的重视。由于对执行措施的报告不充分，以及对此类方法的高度准确性的误导性主张，使情况更加复杂。在本期《JAMA精神病学》中，Gradus及其同事撰写的文章¹ 提出了有关机器学习在研究和实践中的位置的重要问题。