# Big Data in Psychiatry

This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.

This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.

This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.

This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.