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Using Artificial Intelligence and Machine Learning to Promote Child Health Equity.

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Title: Using Artificial Intelligence and Machine Learning to Promote Child Health Equity.
Authors: Cheung, C. Ronny, Butler, Mark, Cooper, Carolyn, Dhanoa, Harpreet
Source: Pediatrics. 2025 Suppl 1, Vol. 156, pS116-S123. 8p.
Subjects: Children's health, Health services accessibility, Risk assessment, Prediction models, Social determinants of health, Artificial intelligence, Socioeconomic factors, Natural language processing, Machine learning, Health promotion, Health equity, Individualized medicine, Case studies, Asthma, Algorithms, Preventive health services, Children
Geographic Terms: England
Abstract: Artificial intelligence (AI) and machine learning (ML), used injudiciously, have the potential to exacerbate health inequalities. Conversely, there is a potential to use ML to give insight into the impact of socioeconomic factors, which allows us to make predictions on an individual as well as a population level. This paper outlines the potential applications of ML in child health, with a specific focus on its impact on health equity. We describe our experience in applying 2 novel ML use cases to promote population health equity in a diverse population in inner-city London, United Kingdom: (1) an ML algorithm developed and trained using routinely collected demographic data to predict nonattendance at outpatient appointments across a wide range of settings and (2) a risk-prediction tool for children with asthma, which uses a number of routine metrics across the spectrum of health determinants to target preventive interventions toward high-risk patients with asthma. Using this experience, we outline the possible ways in which inequity can be inadvertently embedded in the training data, the ML model and how it is deployed and outline ways to mitigate that now and in the future. [ABSTRACT FROM AUTHOR]
Copyright of Pediatrics is the property of American Academy of Pediatrics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Artificial intelligence (AI) and machine learning (ML), used injudiciously, have the potential to exacerbate health inequalities. Conversely, there is a potential to use ML to give insight into the impact of socioeconomic factors, which allows us to make predictions on an individual as well as a population level. This paper outlines the potential applications of ML in child health, with a specific focus on its impact on health equity. We describe our experience in applying 2 novel ML use cases to promote population health equity in a diverse population in inner-city London, United Kingdom: (1) an ML algorithm developed and trained using routinely collected demographic data to predict nonattendance at outpatient appointments across a wide range of settings and (2) a risk-prediction tool for children with asthma, which uses a number of routine metrics across the spectrum of health determinants to target preventive interventions toward high-risk patients with asthma. Using this experience, we outline the possible ways in which inequity can be inadvertently embedded in the training data, the ML model and how it is deployed and outline ways to mitigate that now and in the future. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Pediatrics is the property of American Academy of Pediatrics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=188146620
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1542/peds.2025-070739L
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: S116
    Subjects:
      – SubjectFull: Children's health
        Type: general
      – SubjectFull: Health services accessibility
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Social determinants of health
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Socioeconomic factors
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Health promotion
        Type: general
      – SubjectFull: Health equity
        Type: general
      – SubjectFull: Individualized medicine
        Type: general
      – SubjectFull: Case studies
        Type: general
      – SubjectFull: Asthma
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Preventive health services
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: England
        Type: general
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      – TitleFull: Using Artificial Intelligence and Machine Learning to Promote Child Health Equity.
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            NameFull: Cheung, C. Ronny
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            NameFull: Cooper, Carolyn
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            – D: 02
              M: 09
              Text: 2025 Suppl 1
              Type: published
              Y: 2025
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              Value: 156
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