慢性腎臓病患者のためのAIによって生成された情報の理解性と実行可能性
Emi Furukawa1, Tsuyoshi Okuhara2, Hiroe Suzuki-Chiba3
1University Hospital Medical Information Network (UMIN) Center, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Patient education and counseling
|February 18, 2026
まとめ
慢性腎臓病 (CKD) に対する人工知能 (AI) の反応は理解できるが,専門家の情報と比較して実行可能なアドバイスが欠けている. 医療従事者は,患者の明確さと安全のために,AIによって生成されたCKDコンテンツを精製する必要があります.
科学分野:
- 医療情報工学 医療情報工学
- 医療における人工知能
- 患者教育についてです.
背景:
- 慢性腎臓病 (CKD) 患者の教育は,管理に不可欠です.
- 人工知能 (AI) ツールは,アクセシブルな健康情報への可能性を秘めています.
- 専門家の情報源と比較して,AIによって生成された健康コンテンツを評価することは不可欠です.
研究 の 目的:
- 慢性腎臓病 (CKD) に対するAIによる反応の理解性と実行可能性を評価する.
- 人工知能によって生成されたCKDの情報品質を,専門家によって検証された患者教育資料と比較する.
- CKDの健康情報提供におけるAIの強みと弱点を特定する.
主な方法:
- AIの応答 (ChatGPT,Copilot,Gemini) と専門家CKDのウェブページを比較した定量分析.
- 日本語版の患者教育資料評価ツールを使用しました.
- 180のAI応答と88の専門家アイテムの理解性と実行性を評価しました.
主要な成果:
- 人工知能の回答は,専門家の情報 (67.2% vs 63.4%) と比較可能な理解性を示しました.
- 専門家の情報はAIの回答 (37.1%対27.9%) よりもはるかに有効でした.
- Geminiは,理解性と実行性の両面で他のAIモデルを上回った.
結論:
- AIによって生成されたコンテンツは理解できますが,実行可能性と包括性の改善が必要です.
- AIは医療用語の簡素化に優れているが,論理的な流れや具体的な指示が欠けているかもしれない.
- 医療従事者は,患者の普及前に,AIによって生成されたCKD情報をレビューし,強化する必要があります.
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