Dokument: Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance
| Titel: | Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=68021 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20241220-125028-1 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Reinhart, Lisa [Autor] Bischops, Anne Christine [Autor] Kerth, Janna-Lina [Autor] Hagemeister, Maurus [Autor] Heinrichs, Bert [Autor] Eickhoff, Simon [Autor] Dukart, Juergen [Autor] Konrad, Kerstin [Autor] Mayatepek, Ertan [Autor] Meissner, Thomas [Autor] | |||||||
| Dateien: |
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| Stichwörter: | Child health, Pediatric healthcare, Monitoring, Artificial intelligence, Child development, Pediatrics | |||||||
| Beschreibung: | Objectives
Recent 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 methods The 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. Results Out 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. Conclusion A 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. Previous article in issue | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Reinhart, L., Bischops, A. C., Kerth, J.-L., Hagemeister, M., Heinrichs, B., Eickhoff, S. B., Dukart, J., Konrad, K., Mayatepek, E., & Meißner, T. (2024). Artificial intelligence in child development monitoring: A systematic review on usage, outcomes and acceptance. Intelligence-Based Medicine, 9, Article 100134. https://doi.org/10.1016/j.ibmed.2024.100134 | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Medizinische Fakultät | |||||||
| Dokument erstellt am: | 20.12.2024 | |||||||
| Dateien geändert am: | 20.12.2024 |
