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電子健康記錄アプリケーションにおける生成的大規模言語モデルのテストと評価：系統的レビュー

Xinsong Du^1,2,3, Zhengyang Zhou⁴, Yifei Wang⁴

¹Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, United States.

Journal of the American Medical Informatics Association : JAMIA

|January 13, 2026

まとめ

生成的大規模言語モデル（LLM）はヘルスケア分野で有望視されているが、専門分野やタスクを横断した広範な評価が必要である。現在の評価指標は人間のパフォーマンスとの相関が弱いため、効果的な臨床応用のための標準化されたフレームワークが不可欠である。

背景:

生成的大規模言語モデル（LLM）は、臨床および研究支援のために電子健康記録（EHR）データとともにますます使用されている。この系統的レビューは、ヘルスケアにおけるLLMの現在の応用と評価を特徴づけることを目的としている。

結論:

多様な臨床専門分野およびタスクにわたる生成LLMの広範な評価が必要である。EHRデータにおけるLLMのための標準化され、スケーラブルで、臨床的に意味のある評価フレームワークの開発が急務である。

キーワード:

電子健康記録評価大規模言語モデル自然言語処理レビュー

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