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@ARTICLE{Reinhart:1023470,
      author       = {Reinhart, Lisa and Bischops, Anne C. and Kerth, Janna-Lina
                      and Hagemeister, Maurus and Heinrichs, Bert and Eickhoff,
                      Simon B. and Dukart, Jürgen and Konrad, Kerstin and
                      Mayatepek, Ertan and Meissner, Thomas},
      title        = {{A}rtificial intelligence in child development monitoring:
                      {A} systematic review on usage, outcomes and acceptance},
      journal      = {Intelligence-based medicine},
      volume       = {9},
      issn         = {2666-5212},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-01703},
      pages        = {100134 -},
      year         = {2024},
      abstract     = {ObjectivesRecent advances in Artificial Intelligence (AI)
                      offer promising opportunities for its use in pediatric
                      healthcare. This is especially true for early identification
                      of developmental problems where timely intervention is
                      essential, but developmental assessments are
                      resource-intensive. AI carries potential as a valuable tool
                      in the early detection of such developmental issues. In this
                      systematic review, we aim to synthesize and evaluate the
                      current literature on AI-usage in monitoring child
                      development, including possible clinical outcomes, and
                      acceptability of such technologies by different
                      stakeholders.Material and methodsThe systematic review is
                      based on a literature search comprising the databases
                      PubMed, Cochrane Library, Scopus, Web of Science, Science
                      Direct, PsycInfo, ACM and Google Scholar (time interval
                      1996–2022). All articles addressing AI-usage in monitoring
                      child development or describing respective clinical outcomes
                      and opinions were included.ResultsOut of 2814 identified
                      articles, finally 71 were included. 70 reported on AI usage
                      and one study dealt with users’ acceptance of AI. No
                      article reported on potential clinical outcomes of AI
                      applications. Articles showed a peak from 2020 to 2022. The
                      majority of studies were from the US, China and India (n =
                      45) and mostly used pre-existing datasets such as electronic
                      health records or speech and video recordings. The most used
                      AI methods were support vector machines and deep
                      learning.ConclusionA few well-proven AI applications in
                      developmental monitoring exist. However, the majority has
                      not been evaluated in clinical practice. The subdomains of
                      cognitive, social and language development are particularly
                      well-represented. Another focus is on early detection of
                      autism. Potential clinical outcomes of AI usage and user's
                      acceptance have rarely been considered yet. While the
                      increase of publications in recent years suggests an
                      increasing interest in AI implementation in child
                      development monitoring, future research should focus on
                      clinical practice application and stakeholder's needs.},
      cin          = {INM-7 / INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-11-20170113},
      pnm          = {5255 - Neuroethics and Ethics of Information (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5255 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.ibmed.2024.100134},
      url          = {https://juser.fz-juelich.de/record/1023470},
}