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術前整形外科コミュニケーションの強化:大型言語モデルと臨床医が作成した診療書簡の比較分析
Wilfred C Saunders1, Alexander C Glendenning2, Charles Gamble3
1Emergency Medicine, Wrexham Maelor Hospital, Wrexham, GBR.
Cureus
|February 16, 2026
まとめ
大型言語モデル (LLM) は,文字の読みやすさと合併症の含有性を改善することによって,整形外科患者のコミュニケーションを強化します. LLMによる通信は,知的な同意と共有された意思決定をサポートします.
科学分野:
- 整形外科手術の整形外科手術について
- 医療コミュニケーション 医療コミュニケーション
- 医療における人工知能
背景:
- 効果的なコミュニケーションは,整形外科におけるインフォームド・コンセントに不可欠です.
- 医療制度は,患者との対話に影響を与える労働力や時間的な制約に直面しています.
- 大型言語モデル (LLM) は,患者と臨床医の通信を合理化するための潜在的な解決策を提供します.
研究 の 目的:
- 整形外科の実践におけるLLM支援通信の実現可能性を評価する.
- LLMで生成された文字の理解性,読みやすさ,および合併症プロファイルの含有度を評価する.
- LLMの出力を,ヒトの臨床医が作成した手紙と比較するために.
主な方法:
- 6つの一般的な整形外科手術が選択されました.
- 4つのLLM (OpenAI o1,DeepSeek,Gemini,Copilot) と2人の臨床医を通じて手紙を生成するために標準化されたプロンプトが使用されました.
- 理解度 (PEMAT-P),読解度 (Flesch-Kincaid, Gunning Fog, SMOG),および合併症を含むことが測定されました.
主要な成果:
- LLMで生成された手紙は,臨床医の手紙と比較して,より優れた理解性と読解性を示しました.
- OpenAI o1 と Gemini は高合併症プロフィール適合を示しました.
- LLMのテキストは7年生から8年生までの読解レベルで書かれており,臨床医の手紙よりも単純です.
結論:
- LLMは,明確さと完全性において,従来の臨床医の書簡を上回ることができます.
- LLMのワークフローを統合することで,管理上の負担を軽減し,患者の意思決定を改善することができます.
- LLMの倫理的で安全な臨床展開のためにさらなる研究が必要である.
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