{"title":"Artificial Intelligence and Social Media for the Detection of Eating Disorders.","authors":"Jinbo He, Feng Ji","doi":"10.1002/eat.24438","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) has been increasingly recognized for its potential in mental health management, including detecting, preventing, and treating eating disorders (EDs). Linardon et al. investigated current practices and perspectives on AI in ED treatment from professionals and community participants. While their work provides valuable insights into AI's role in ED management in the treatment phase, the applications of AI at earlier stages, particularly for case detection, and perspectives of key groups involved in this early-stage implementation (e.g., health professionals and individuals with or at risk of EDs) remain underexplored. Given the large volume of multimodal data available on social media platforms, together with their widespread use and accessibility, the integration of AI and social media provides an ideal opportunity for conducting large-scale, population-based detection for EDs. Thus, in this commentary, we discuss AI's potential to leverage social media data for case detection, highlight related ethical considerations (e.g., bias and data privacy), and propose future research directions.</p>","PeriodicalId":51067,"journal":{"name":"International Journal of Eating Disorders","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Eating Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/eat.24438","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Artificial intelligence (AI) has been increasingly recognized for its potential in mental health management, including detecting, preventing, and treating eating disorders (EDs). Linardon et al. investigated current practices and perspectives on AI in ED treatment from professionals and community participants. While their work provides valuable insights into AI's role in ED management in the treatment phase, the applications of AI at earlier stages, particularly for case detection, and perspectives of key groups involved in this early-stage implementation (e.g., health professionals and individuals with or at risk of EDs) remain underexplored. Given the large volume of multimodal data available on social media platforms, together with their widespread use and accessibility, the integration of AI and social media provides an ideal opportunity for conducting large-scale, population-based detection for EDs. Thus, in this commentary, we discuss AI's potential to leverage social media data for case detection, highlight related ethical considerations (e.g., bias and data privacy), and propose future research directions.
期刊介绍:
Articles featured in the journal describe state-of-the-art scientific research on theory, methodology, etiology, clinical practice, and policy related to eating disorders, as well as contributions that facilitate scholarly critique and discussion of science and practice in the field. Theoretical and empirical work on obesity or healthy eating falls within the journal’s scope inasmuch as it facilitates the advancement of efforts to describe and understand, prevent, or treat eating disorders. IJED welcomes submissions from all regions of the world and representing all levels of inquiry (including basic science, clinical trials, implementation research, and dissemination studies), and across a full range of scientific methods, disciplines, and approaches.