電子医療記録アプリケーションのための生成的な大型言語モデルの適応のためのパフォーマンスと改善戦略:体系的なレビュー
Xinsong Du1, Zhengyang Zhou2, Yifei Wang2
1Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, United States; Department of Medicine, Harvard Medical School, Boston, MA 02115, United States.
International journal of medical informatics
|August 30, 2025
まとめ
電子医療記録 (EHR) の分析のための生成的な大型言語モデル (LLM) の適応は有望ですが,パフォーマンスの改善戦略には,さらなる定量的な評価と,効果的かつ安全な適用のための明確なガイドラインが必要です.
科学分野:
- 生物医学情報学
- 医療における人工知能
- 医療データ科学
背景:
- 発電型大型言語モデル (LLM) は,電子医療記録 (EHR) を分析する可能性を秘めています.
- EHRデータにLLMを適応させるには,効果的なパフォーマンスと改善戦略が必要です.
- 現在の戦略とその評価された影響の体系的な統合が必要である.
研究 の 目的:
- EHR分析における生成的なLLMの適応のためのパフォーマンスと改善戦略を統合する.
- 主要な臨床応用を特定し,報告された性能評価を要約する.
主な方法:
- PRISMAのガイドライン (PubMed, Web of Science,2023年1月から2024年11月まで) に従った体系的なレビュー.
- 定量的なパフォーマンスの評価を伴う現実世界のEHRデータ分析のための生成的なLLMを使用した研究を含みます.
- 審査員には生物医学情報工と臨床医がいました
主要な成果:
- 196件が基準を満たし,57. 1%が臨床決定支援に,20. 4%が文書化に重点を置いた.
- ほとんどの研究 (88. 8%) は改善戦略を定量的に評価しなかった.
- Few-shot プロンプト,フィーヌチューニング,マルチモダルの統合は,混雑した結果を示した.より小さなフィーヌチューニングされたモデルは,時にはより大きなものを上回った.
結論:
- EHRにおけるLLMのパフォーマンス改善戦略は,改善された結果を保証するものではありません.
- これらの戦略を有効かつ安全に適用するために,詳細で証拠に基づいたガイドラインが必要である.
- 定量的な分析は,これらのガイドラインの開発に不可欠です.
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