医療における機械学習アプリケーションの経済的側面に関するスコーピングレビュー
Hanna von Gerich1, Mikael Helenius2, Iiris Hörhammer3
1Department of Nursing Science, University of Turku, Finland; Department of Health and Social Management, University of Eastern Finland, Finland.
International journal of medical informatics
|September 2, 2025
まとめ
このレビューは,医療における人工知能 (AI) と機械学習 (ML) の経済的価値の評価におけるギャップを明らかにしています. 現在の研究では,AIの特異性と経済的な詳細に関する包括的な報告が欠けていて,証拠に基づいた意思決定を妨げています.
科学分野:
- 健康 経済
- 医療における人工知能
- 機械学習アプリケーション
背景:
- 医療におけるAIとMLの利用が拡大しているため,安全性や経済的価値に関する証拠が必要です.
- 経済的な評価は医療の意思決定と 資源の配分に不可欠です
- このスコーピングレビューは,医療におけるML技術の経済的評価のための現在の方法をまとめています.
研究 の 目的:
- 医療における機械学習ベースの技術の経済的評価に関する既存のアプローチをマッピングし,統合する.
- 医療AIの経済評価の報告とカバーのギャップを特定する.
- CHEERS-AIのような報告ガイドラインと現在の研究の整合性を評価する.
主な方法:
- JBIのガイドラインに沿った包括的なスコーピングレビュー.
- 6つの主要なデータベース (PubMed,CINAHL,Cochrane,Embase,Scopus,IEEE Xplore) で検索しました
- 分析された18件は,記述的統計と誘導的コーディングを用いた研究を含み,CHEERS-AIの記述と比較した.
主要な成果:
- 経済的評価,影響評価,パフォーマンス評価を含む18の研究が含まれていた.
- 費用対効果分析は最も一般的な経済評価方法でした.
- CHEERS-AIとの平均一致はわずか39.6%で,AI特有の詳細については特に低い (21.3%) 報告のギャップが見つかりました.
結論:
- 医療における機械学習技術の経済評価に関する現在の文献は,一貫性や包括的なカバーが欠けている.
- システムと実装後の評価を含むAI開発フレームワークの精錬が必要である.
- 健全な経済評価を行うためには,多分野間の協力を強化することが不可欠です.
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