AIにおける革新的なソリューションによる抗菌剤耐性の緩和 (MARISA):修正されたジェームズ・リンド・アライアンス分析
William J Waldock1, Hannah Thould1, Leonid Chindelevitch2
1Institute of Global Health Innovation, Imperial College London, London, UK.
npj antimicrobials and resistance
|September 1, 2025
まとめ
人工知能 (AI) は抗菌剤耐性 (AMR) と戦うことができますが,研究優先順位は不明です. この研究では,この世界的な健康上の脅威に対する将来の取り組みを導くために,新しい治療法と診断法を含むAI-AMRの主要な研究分野を特定しました.
科学分野:
- 医療情報学
- 感染症
- 公衆衛生
背景:
- 抗菌剤耐性 (AMR) は,世界的な健康問題です.
- 人工知能 (AI) は抗生物質耐性に対抗するための新しいアプローチを提供していますが,この交差点での研究優先順位は未定義です.
研究 の 目的:
- AIとAMRの交差点にある主要な研究分野を特定し,優先順位付けする.
- AI-AMRイニシアチブの将来の研究方向を定義する.
主な方法:
- 修正されたジェームズ・リンド・アライアンスアプローチが採用された.
- AIとAMRの専門家8人と半構造面接が行われました.
- 338件のコード化された回答を分析した結果 44の異なるテーマが特定されました
主要な成果:
- 特定された主な障壁は,分散したデータアクセス,統合の課題,経済的な阻害要因です.
- トップ10の研究優先事項には,組み合わせ療法,新型治療法,データ取得,AMR公衆衛生政策,優先順位付け,経済資源の配分,診断,微生物進化のモデル化,AMR予測,および監視が含まれています.
- BARDIフレームワーク (仲介データ共有,AI駆動モデリング,迅速診断,薬物発見,統合経済予防) が提案されました.
結論:
- 研究の優先順位を決めることは,抗生物質耐性におけるAIアプリケーションの進歩に不可欠です.
- 効果的なAI-AMR戦略には,データアクセス,経済要因,地域的代表性への対応が不可欠です.
- BARDIの枠組みは,将来のAI-AMRの研究と実施を導くための構造的なアプローチを提供します.
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