解剖学および臨床病理学の構造特征抽出のための大型言語モデルの評価 解剖学および臨床病理学のレポート
Brody H Foy1, Kelly D Smith1, Olivia L Vargas1
1Department of Laboratory Medicine & Pathology, University of Washington Medicine, Seattle, WA, United States.
Clinical chemistry
|February 12, 2026
まとめ
大型言語モデル (LLM) は,病理学ノートから構造的な特徴を抽出するのに高い精度を示し,自動化された臨床データ分析のための貴重なツールを提供します. これらのLLMツールは,手動チャートレビューに関連する時間とコストを大幅に削減します.
科学分野:
- 医療情報工学 医療情報工学
- 自然言語処理 (Natural Language Processing) とは,自然言語処理で処理される言語のことです.
- コンピューター病理学 (Computational Pathology) について
背景:
- 臨床ノートから手動で特徴を抽出することは,時間と費用がかかります.
- 自然言語処理 (NLP) の進歩は,高通量機能抽出のための自動化されたソリューションを提供します.
- 大型言語モデル (LLM) は,病理学における構造的特征抽出に希望を示しています.
研究 の 目的:
- 臨床病理学および解剖病理学ノートから構造的特徴抽出のためのLLMの精度を評価する.
- 様々な病理学データセットの専門家臨床医のラベルに対してLLMのパフォーマンスを比較する.
- 構造化された機能の急速なプロトタイピングのためのツールを開発するには,LLMに依頼されます.
主な方法:
- OpenAIのGPT-4oおよびGPT-5モデルを評価し,心臓移植の病理学,ヘモグロビン変異体,尿薬試験報告書を評価した.
- LLMで抽出した特徴と専門医のマニュアルラベルを比較した.
- LLMに構造化された関数呼び出しを設計するためのWebアプリケーション ("toolbuilder") を開発しました.
主要な成果:
- LLMは高い精度を達成し,エラー率は単純ケースでは5%近く,複雑なケースでは10%でした.
- 専門家とLLMの一致度は高かった (κ>0.9),専門家間の合意に近い.
- モデルの性能はタイプによって異なるが,プロンプトエンジニアリングに対する感度が低く,誤りはしばしばネガティブと不確定の発見を区別することを含む.
結論:
- LLMベースのツールは,臨床ノートから時間とコストを要する機能抽出を自動化することができます.
- これらの自動化された方法は,臨床ケアと研究にとって重要な価値を持っています.
- LLMは,病理学データの注釈と分析を簡素化する可能性を示しています.
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