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

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Bibliographic Details
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]
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Database: Psychology and Behavioral Sciences Collection
Description
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]
ISSN:00314005
DOI:10.1542/peds.2025-070739L