集中治療医療における結果予測における生成型人工知能の応用 - スコーピングレビュー
Tanja Stamm1,2, Mohamed Bader-El-Den2, James McNicholas3
1Institute of Outcomes Research, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Frontiers in digital health
|August 21, 2025
まとめ
ジェネラティブAIは 集中治療の結果を予測し 治療決定に役立ちます このレビューでは,Generative Adversarial NetworksとGenerative Pretrained Transformersが分野をリードしているデータ増強,機能生成,および直接予測におけるその使用が強調されています.
科学分野:
- 集中治療 医療
- 人工知能
- 機械学習
背景:
- 最初の24時間生き残った集中治療の患者にとって,正確な結果の予測は不可欠です.
- 人工知能 (AI) は医療の予測において 臨床医の能力を越えています
- 生産的なAI技術は 過去10年間で急速に発展しています
研究 の 目的:
- 集中治療医療における結果予測のための生成AIモデルの適用に関するスコーピングレビューを実施する.
- 予測におけるジェネラティブAIの用例に基づいて,特定された研究を分類する.
主な方法:
- 文書 の 徹底 的 な 調べ が 行なわ れ,481 件 の 記録 が 発見 さ れ まし た.
- 抽象的なスクリーニングと全文レビューが行われました.
- 最終分析には22件の研究と2件のレビュー記事が含まれていた.
主要な成果:
- ジェネラティブAIの主な利用例は3つ:データ増強,非構造データからの機能生成,直接予測です.
- データ増強と機能生成のための下流モデルと並行して生成モデルが使用されました.
- 第3のユースケースでは,ジェネラティブモデルが直接予測を行いました.
- 発電対抗ネットワーク (GAN) と発電前訓練トランスフォーマー (GPT) が最も一般的な技術でした.
- ほとんどの出版物 (21/22) は過去4年のもので,最近の研究が急増していることを示しています.
結論:
- ジェネラティブAIは,集中治療における結果予測を向上させる大きな可能性を秘めています.
- 患者ケアを最適化するために,新興の生成AI技術の継続的なモニタリングは不可欠です.
- これらの高度なAIモデルのさらなる研究と応用は 集中治療施設で正当化されています
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