医学研究文書の手書きフォームの転写
Luca Kohlhepp1, Sabine Busies2, Ute Zirrgiebel2
1CAIDAS (Center of AI and Data Science), Univ. Würzburg, Germany.
Studies in health technology and informatics
|September 3, 2025
まとめ
光学文書認識と大型言語モデル (LLM) を使用した臨床試験マスターファイル (eTMF) 文書の自動転写は,データ入力時間を大幅に短縮します. この技術は,手動の転写方法と比較してかなりの時間を節約します.
科学分野:
- 臨床データ管理
- 医療における人工知能
背景:
- 電子臨床試験マスターファイル (eTMF) に手動でデータを入力することは,労苦的で時間のかかるプロセスです.
- 臨床試験の文書の効率的な管理は,規制の遵守と運用効率に不可欠です.
研究 の 目的:
- eTMFのデータベースを充填するための自動転写方法の有効性を評価する.
- 文書分析のための光学文書認識 (ODR) と多式大型言語モデル (LLM) の性能を比較する.
主な方法:
- 光学文書認識 (ODR) パイプラインの開発と適用
- ウェブベースの (グローバル) およびローカルマルチモダルの大型言語モデル (LLM) の実装とテスト.
- 複写の精度と効率の実験的検証
主要な成果:
- この研究では,異なる自動化されたアプローチが,テーブルベースのドキュメント内のさまざまな列タイプに最適であることを示しました.
- 光学文書認識とLLMは,臨床試験の文書から情報を正確に転写するのに有望です.
- 特定の文書の構造と内容によってパフォーマンスが異なる.
結論:
- ODRとLLMを用いた自動トランスクリプションは,従来の手動のデータ入力と比較して,かなりの時間の節約をもたらします.
- この結果は,eTMFのデータ管理を効率化するための実用的な技術的解決策を示唆しています.
- LLMとODRのアプローチのさらなる最適化により,臨床試験の効率が向上します.
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