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@ARTICLE{Kerth:1034442,
      author       = {Kerth, Janna-Lina and Hagemeister, Maurus and Bischops,
                      Anne C. and Reinhart, Lisa and Dukart, Juergen and
                      Heinrichs, Bert and Eickhoff, Simon B. and Meissner, Thomas},
      title        = {{A}rtificial intelligence in the care of children and
                      adolescents with chronic diseases: a systematic review},
      journal      = {European journal of pediatrics},
      volume       = {184},
      number       = {1},
      issn         = {0340-6199},
      address      = {Dordrecht},
      publisher    = {Springer Science $\&$ Business Media B.V.},
      reportid     = {FZJ-2024-07208},
      pages        = {83},
      year         = {2025},
      abstract     = {The integration of artificial intelligence (AI) and machine
                      learning (ML) has shown potential for various applications
                      in the medical field, particularly for diagnosing and
                      managing chronic diseases among children and adolescents.
                      This systematic review aims to comprehensively analyze and
                      synthesize research on the use of AI for monitoring,
                      guiding, and assisting pediatric patients with chronic
                      diseases. Five major electronic databases were searched
                      (Medline, Scopus, PsycINFO, ACM, Web of Science), along with
                      manual searches of gray literature, personal archives, and
                      reference lists of relevant papers. All original studies as
                      well as conference abstracts and proceedings, focusing on AI
                      applications for pediatric chronic disease care were
                      included. Thirty-one studies met the inclusion criteria. We
                      extracted AI method used, study design, population,
                      intervention, and main results. Two researchers
                      independently extracted data and resolved discrepancies
                      through discussion. AI applications are diverse,
                      encompassing, e.g., disease classification, outcome
                      prediction, or decision support. AI generally performed
                      well, though most models were tested on retrospective data.
                      AI-based tools have shown promise in mental health analysis,
                      e.g., by using speech sampling or social media data to
                      predict therapy outcomes for various chronic conditions.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39672974},
      UT           = {WOS:001379523700001},
      doi          = {10.1007/s00431-024-05846-3},
      url          = {https://juser.fz-juelich.de/record/1034442},
}