グローバル・ヘルスにおけるデータ・サイエンスと人工知能の研究の優先事項: 国際的なコンセンサス・エクササイズ
Peige Song1, Denan Jiang2, Jiali Zhou2
1School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
The Lancet. Global health
|February 19, 2026
まとめ
人工知能 (AI) とデータサイエンス (data science) は世界の健康を向上させることができるが,研究は低・中所得国 (LMICs) のニーズに合わせなければならない. この研究では,LMICsにおける疫病の準備,診断,および健康の平等のためにAI研究を優先しました.
科学分野:
- グローバル・ヘルス グローバル・ヘルス
- データサイエンス データサイエンス
- 人工知能 (AI) について
背景:
- 世界保健におけるデータサイエンスとAIの応用は増加しているが,断片化している.
- 研究は,低・中所得国 (LMICs) のニーズとよく一致していない.
研究 の 目的:
- グローバル・ヘルスにおけるAIとデータサイエンスのためのグローバル・リサーチ・プライオリティ・セッティング・エクササイズを実施する.
- 研究をLMICの特定のニーズと優先順位に整えること.
主な方法:
- 子どもの健康と栄養研究イニシアチブ (CHNRI) の方法を使用しました.
- 実現可能性,影響,公平性に基づいて155の研究アイデアをスコアするために51人のグローバルエキスパートを募集しました.
主要な成果:
- トップの優先事項は,流行の準備 (アウトブレイク予測,診断,早期警告システム) のためのAIです.
- その他の主要分野は,人工知能による資源配置,遠隔医療,移動医療,慢性疾患管理です.
- LMICの専門家は感染症と診断の公平性を優先し,高所得国の専門家はインフラと気候分析に焦点を当てた.
結論:
- この研究は,AIとデータサイエンスの研究を世界の保健優先事項と整合させるためのロードマップを提供します.
- 公平な研究アジェンダの必要性を強調し,特にLMICsに利益をもたらす.
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