ガイドラインに基づいているが,エラーがないわけではない: 胆石に関するAI対応の患者カウンセリングにおける多言語リスク
Olgun Erdem1, Tolga Canbak1, Aylin Acar1
1University of Health Sciences, Umraniye Training and Research Hospital, Department of General Surgery, Istanbul, Turkiye.
International journal of medical informatics
|February 14, 2026
まとめ
大型言語モデル (LLM) は健康情報を提供しますが,その正確さは言語とモデルによって異なります. LLMからの胆石のアドバイスは,ガイドラインに沿ったものかもしれませんが,特にトルコ語ではリスクがあります.
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:
- 医療情報のための大型言語モデル (LLM) の患者の使用が増加しています.
- ガイドラインの一致性とLLMの出力の安全性に関する不確実性,特に多言語の文脈で.
- 患者向きの健康カウンセリングのためのLLMプラットフォームの評価の必要性.
研究 の 目的:
- 胆石関連のカウンセリングのためのLLMアウトプットのガイドラインコンコンダンスと安全性を評価する.
- 英語とトルコ語の異なるLLMプラットフォーム (ChatGPT,Gemini,Perplexity) のパフォーマンスを比較する.
- LLMの回答における言語依存の違いを特定する.
主な方法:
- トルコ語と英語で14件の患者質問の横断的な内容分析.
- ChatGPT (GPT-4o mini),Gemini (3-flash),Perplexity (Sonar) のLLM応答は,2人の外科医によって独立して評価されました.
- EASL 2016と東京ガイドライン 2018を使用してガイドラインコンコンダンスが評価され,エラータイプと応答の長さが分析されました.
主要な成果:
- 英語では,ChatGPTとGeminiがPerplexityを大幅に上回った.
- ChatGPTは,トルコ語と比較して英語でより良いパフォーマンスを示しました.
- 言語に依存するエラープロファイル:トルコ語の回答はしばしば説明不足であり,英語の回答はリスクを増幅した.
結論:
- 胆石のクエリに対するLLMの応答は,モデルと言語に敏感であり,臨床的に重要な安全リスクをもたらします.
- 医療におけるLLMのための多言語評価基準は不可欠です.
- 患者の指導のためのLLMへの無監督の依存を,特に低リソースの言語で抑える.
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