人工知能と機械学習を用いて子どもの健康の公平性を促進する
C Ronny Cheung1,2, Mark Butler1,2, Carolyn Cooper3
1Evelina London Children's Hospital, London, United Kingdom.
Pediatrics
|August 31, 2025
まとめ
人工知能 (AI) と機械学習 (ML) は健康格差を悪化させながらも 子どもの健康の平等を促進します この研究では,診察の欠席を予測し,標的を絞った介入のために高リスクの喘息患者を特定するためのMLの応用が検討されています.
科学分野:
- 小児の健康
- 健康に関する公平性
- コンピュータ・ヘルス
背景:
- 人工知能 (AI) と機械学習 (ML) は 注意深く使わないと 健康上の不平等を悪化させる可能性があります
- MLは健康に影響を与える社会経済的要因の潜在的洞察を提供し,個人および集団レベルの予測を可能にします.
- 子どもの健康における健康の公平性を重視するには AI/MLの適用を慎重に検討する必要があります.
研究 の 目的:
- 子どもの健康におけるMLの潜在的応用を概説し,健康の公平性への影響に焦点を当てます.
- 多様な都市環境における人口健康の公平性を促進するための2つの新しいML用例を記述する.
- MLトレーニングデータ,モデル,および展開に埋め込まれた潜在的な不平等を特定し,軽減する.
主な方法:
- 外来診療の欠席率を予測するための日常的な人口統計データを用いたMLアルゴリズムの開発とトレーニング
- 小児性喘息のリスク予測ツールの作成
- 都市部の多様な住民に MLの適用
主要な成果:
- 子どもの健康診察の欠席率を予測する上で MLの有用性を実証した.
- 予防的な介入のために高リスクの喘息患者を特定するためのツールを開発しました.
- 医療の公平性を向上させるための MLの潜在力を強調した.
結論:
- 子どもの健康に関する公平性を 発展させ 慎重に活用する 可能性が高いのです
- MLシステムに不公平が意図的に埋め込まれるのを防ぐために 積極的な戦略が必要である.
- 緩和戦略は,AI/MLがすべての子供に平等に恩恵を与えるようにするために不可欠です.
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