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DIALOGUE: 仮想型2型糖尿病シナリオを通じて医学生の診断コミュニケーションを強化するAIベースの生成前後シミュレーション研究
Ricardo Xopan Suárez-García1,2, Quetzal Chavez-Castañeda2, Rodrigo Orrico-Pérez2
1Unidad de Remisión de Diabetes Mellitus (URDM), Facultad de Estudios Superiores-Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico.
まとめ
生成型人工知能 (GenAI) 訓練は,特に2型糖尿病の診断を説明する際に,医学生の診断コミュニケーションスキルを大幅に改善しました. DIALOGUE プログラムは 患者との出会いにおいて 明確さ,構造,共感を高めました
科学分野:
- 医学教育
- 医療における人工知能
- 診断コミュニケーション
背景:
- 効果的な診断コミュニケーションは 患者の成果にとって極めて重要です
- 伝統的な医療訓練では 必要なコミュニケーション能力が 十分に備わっていないかもしれません
- 生産性のある人工知能 (GenAI) は新しいトレーニング方法を提供します.
研究 の 目的:
- 医学生の診断コミュニケーションスキルを向上させるためのDIALOGUE (客観的なユーザー体験によるDIagnostic AI Learning) プログラムの有効性を評価する.
- GenAIによる研修が2型糖尿病 (T2DM) の診断を明らかにする学生の能力に与える影響を評価する.
- 診断コミュニケーションの改善に影響を与える要因を特定する.
主な方法:
- 30人の臨床段階の医学生を対象とした 単腕前後試験の設計です
- 盲目評価者によって得点されたシミュレートされた患者との出会いを用いた試験前および試験後の評価
- DIALOGUEプログラムを使用した自動フィードバックを持つ10の非同期的なGenAIトレーニングシナリオ.
- コミュニケーションの8つの領域と影響因子のパフォーマンスの分析
主要な成果:
- 診断コミュニケーションの全体的なパフォーマンスの有意な改善 (平均 36. 7 ポイント,p < 0. 001)
- 優秀な学生の割合は 0%から70%に増加しました
- すべての領域で利益が観察され,遭遇の開閉および糖尿病特有の説明で顕著な改善が見られた.
結論:
- DIALOGUEプログラムで示されたGenAIによる研修は,医学生の診断コミュニケーションスキルを大幅に向上させることができます.
- このプログラムは従来の医療教育に 拡張可能で個別化された補足として 可能性を秘めています
- 学習者の基本的共感やデジタル自己効能といった特徴が 訓練結果に影響を与えました
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