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医学生のための人工知能（AI）リテラシー能力リストと評価フレームワーク（2025年版）に関する専門家コンセンサス

Meng-Chun Gong1, Hui Pan2, Hui Liu3

  • 1Guangdong Medical University,Dongguan,Guangdong 523808,China.

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
|February 6, 2026
PubMed
まとめ

このコンセンサスは、医学生のための人工知能（AI）リテラシー能力リストと評価フレームワークの概要を示しています。AI主導の医療環境のために将来の医療専門家を育成することを目的としています。

キーワード:
人工知能人工知能能力臨床能力医学教育医学生

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科学分野:

  • 医学教育
  • ヘルスケアにおける人工知能
  • 能力ベース教育

背景:

  • インテリジェントヘルスケアの急速な進歩は、AIリテラシーを持つ医療専門家のトレーニングを必要とします。
  • 既存の医学教育フレームワークは、AI能力を統合するために適応が必要です。
  • 能力ベースの医学教育（CBME）は、AIに対応できる臨床医を育成するための基盤を提供します。

研究 の 目的:

  • 医学生のための包括的なAIリテラシー能力リストを開発すること。
  • 医学教育におけるAI能力を評価するための実践的な評価フレームワークを確立すること。
  • インテリジェントヘルスケアのためにAIをカリキュラムに統合する上で医学部を導くこと。

主な方法:

  • 多分野にわたる専門家パネルによる2ラウンドのデルファイ法。
  • 能力開発のための情報を提供する体系的な文献レビュー。
  • 能力モデルとミラーの臨床能力ピラミッドの統合。

主要な成果:

  • 知識（8）、スキル（8）、態度（5）に分類された、医学生のための21の指標からなるAIリテラシー能力リスト。
  • 標準化テスト、状況判断テスト、AIシナリオベースの客観的構造化臨床試験を含む、提案された評価システム。
  • 入学、臨床前、臨床訓練段階にわたる縦断的評価戦略を推奨。

結論:

  • 開発されたAI能力リストと評価フレームワークは、科学的厳密性と実践的な適用性を備えています。
  • このコンセンサスは、AIを教育に統合する医学部にとって重要な参照となります。
  • インテリジェントヘルスケア時代のための複合的な医療人材の育成を支援します。