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The use and potential of artificial intelligence for supporting clinical observation of child behaviour.

Background: Observation of child behaviour provides valuable clinical information but often requires rigorous, tedious, repetitive and time expensive protocols. For this reason, tests requiring significant time for administration and rating are rarely used in clinical practice, however useful and ef...

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Published in:Child & Adolescent Mental Health Vol. 29; no. 4; pp. 340 - 345
Main Authors: Minnis, Helen, Vinciarelli, Alessandro, Alsofyani, Huda
Format: research tables/charts Journal Article
Published: Wiley-Blackwell Nov2024
Online Access:View this record in EBSCOhost
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      jtl: Child & Adolescent Mental Health
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      dt: Nov2024
      vid: 29
      iid: 4
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1111/camh.12714
        180474853
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        atl: The use and potential of artificial intelligence for supporting clinical observation of child behaviour.
      aug:
        au:
          Minnis, Helen
          Vinciarelli, Alessandro
          Alsofyani, Huda
        affil: Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
      sug:
        subj:
          Attachment Behavior Evaluation
          Child Behavior Evaluation
          Psychological Tests In Infancy and Childhood
          Task Performance and Analysis Methods
          Artificial Intelligence Utilization
          Child Behavior Disorders Symptoms
          Human
          Funding Source
          Storytelling
          Child, Preschool
          Child
          Artificial Intelligence Ethical Issues
          Mental Health Evaluation
          Child Health
          Mental Disorders Diagnosed in Childhood
          Child, Preschool: 2-5 years
          Child: 6-12 years
      ab: Background: Observation of child behaviour provides valuable clinical information but often requires rigorous, tedious, repetitive and time expensive protocols. For this reason, tests requiring significant time for administration and rating are rarely used in clinical practice, however useful and effective they are. This article shows that Artificial Intelligence (AI), designed to capture and store the human ability to perform standardised tasks consistently, can alleviate this problem. Case study: We demonstrate how an AI‐powered version of the Manchester Child Attachment Story Task can identify, with over 80% concordance, children with insecure attachment aged between 5 and 9 years. Discussion: We discuss ethical issues to be considered if AI technology is to become a useful part of child mental health assessment and recommend practical next steps for the field.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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