人間の認知機能障害と言語モデルによる認知機能障害の判断:説明可能な人工知能アプローチ
Maya Zadok1, Lotem Peled-Cohen1, Nitay Calderon1
1Faculty of Data and Decisions Sciences Technion- Israel Institute for Technology Haifa Israel.
Alzheimer's & dementia (Amsterdam, Netherlands)
|January 26, 2026
まとめ
専門家ではない人間と大規模言語モデル(LLM)は、テキスト中の認知症の手がかりを一貫して特定することに苦労している。LLMは、人間よりも広範な言語的信号を使用して認知症スクリーニングの可能性を示している。
科学分野:
- 認知科学
- 人工知能
- 言語学
背景:
- テキスト中の認知症の兆候に対する人間の認識は、早期の医学的評価にとって重要である。
- 大規模言語モデル(LLM)は、認知機能低下のアクセス可能なスクリーニングツールとしての可能性を提供する。
研究 の 目的:
- 非専門家である人間とLLMが、書き起こされたテキストにおける認知症関連の言語的手がかりをどのように認識するかを比較すること。
- LLMを潜在的な認知症スクリーニングツールとして評価すること。
主な方法:
- 書き起こされた絵の説明を、認知症の兆候について人間とLLMが評価した。
- 専門家が指導した言語的特徴とロジスティック回帰を使用してテキストを分析した。
主要な成果:
- 人間の判断は一貫性がなく、限定的で、時には不正確な手がかりに依存していた。
- LLMは人間よりも包括的で臨床的に関連性の高い特徴セットを利用した。
- 人間とLLMの両方が、認知症検出において偽陰性の傾向を示した。
結論:
- 多様な認知症関連言語信号に対する人間とLLMの両方の認識を強化する必要がある。
- 説明可能なAIは、認知症に関連する言語パターンを理解し、早期検出を改善するために価値がある。
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