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大規模言語モデルによるエビデンスに基づく臨床推奨事項の合理化

Dubai Li1, Nan Jiang2, Kangping Huang2

  • 1College of Biomedical Engineering and Instrument Science, Zhejiang University, Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Hangzhou, China.

NPJ digital medicine
|December 21, 2025
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まとめ
この要約は機械生成です。

本研究では、臨床エビデンスの統合と推奨事項の生成を迅速化するAIシステム「Quicker」を紹介します。Quickerは、臨床医がより迅速かつ確実にエビデンスに基づく医療意思決定を行えるよう支援します。

キーワード:
大規模言語モデル臨床意思決定支援エビデンスに基づく医療AI臨床推奨事項

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科学分野:

  • ヘルスケアにおける人工知能;臨床意思決定支援システム;エビデンスに基づく医療

背景:

  • ワークロードと時間の制約により、臨床エビデンスを実践に統合することは困難です。現在のエビデンス統合方法は時間がかかり、リソースを大量に消費します。

研究 の 目的:

  • 臨床エビデンスの統合と推奨事項の生成を自動化するための大規模言語モデル（LLM）搭載システムであるQuickerを開発および評価すること。標準的なガイドライン開発ワークフローを複製するQuickerの能力を評価すること。

主な方法:

  • 臨床的な質問から推奨事項までのエンドツーエンドのパイプラインであるQuickerを開発しました。3つの疾患のガイドライン開発記録から作成されたベンチマークデータセットであるQ2CRBench-3を作成しました。効率と精度を評価するために、参加者によるシステムレベルのテストを実施しました。

主要な成果:

  • Quickerは、正確な質問分解、専門家と一致したエビデンス検索、包括的なスクリーニングを実証しました。支援されたデータ抽出は精度を向上させました。生成された推奨事項は、臨床医が作成したものよりも包括的で一貫性がありました。システムレベルのテストでは、Quickerが推奨事項の開発時間を参加者あたり20〜40分に短縮することが示されました。

結論:

  • Quickerは、エビデンスに基づく臨床意思決定の速度と信頼性を大幅に向上させます。LLM搭載システムは、ガイドライン開発プロセスを合理化する可能性を示しています。Quickerは、統合されたツールとインタラクティブなインターフェイスを通じて、カスタマイズされた意思決定をサポートします。