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.
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Fazel S, O’Reilly L. Machine Learning for Suicide Research–Can It Improve Risk Factor Identification? JAMA Psychiatry. 2020;77(1):13–14. doi:10.1001/jamapsychiatry.2019.2896
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