ChatGPT,Gemini,DeepSeekのパフォーマンスは,緊急部での現実の会話を使用して,非重要なトリアージのサポートです
Sukyo Lee1, Sumin Jung2, Jong-Hak Park1
1Department of Emergency Medicine, Korea University Ansan Hospital, Ansan-si, 15355, Republic of Korea.
BMC emergency medicine
|September 1, 2025
まとめ
大規模な言語モデル (LLM) は,緊急治療室 (ED) のトリアージに希望を示し,実際の会話から患者の緊急性を正確に評価します. いくつかのモデルは,高感度と迅速な応答時間を提供し,非重要なトリアージツールの使用をサポートしています.
科学分野:
- 医療における人工知能
- 臨床的意思決定支援システム
- 緊急医療情報学
背景:
- 正確な緊急治療室 (ED) のトリアージは患者のケアに不可欠です.
- 大型言語モデル (LLM) は,トリアージの意思決定を支援するために検討されています.
- 既存のLLMの評価は,現実の臨床症例ではなく,シミュレートされたデータを使用します.
研究 の 目的:
- 非重症性ED患者のトリアージングにおける商用LLMの性能を評価する.
- シミュレートされたシナリオではなく 現実の臨床会話を用いて LLMの精度を評価します
- 複数のLLMをゼロショットと数ショットのプロンプト条件で比較する.
主な方法:
- 韓国の病院での実際のトリアージ会話を 振り返って分析したものです
- 商用LLM (例えば,GPT-4o,Gemini 2.5 flash/pro) の対話ベースの緊急性分類に関する評価
- 黄金基準として韓国のトリアージ・アンド・アキュティ・スケール (KTAS) と比較.
主要な成果:
- Gemini 2.5フラッシュは最高精度 (73. 8%) と特異性 (88. 9%) を達成しました.
- Gemini 2.5 proは最高感度 (90. 9%) とF1スコア (82. 4%) を示した.
- GPT-4.1は,バランスのとれた精度 (70.6%),感度 (81.3%),および迅速な応答時間 (1.79s) を提供した.
結論:
- LLMは現実の臨床会話から ED患者の緊急性を正確に分類できます
- モデルでは,高感度で許容可能な応答時間を備えた非重要なトリアージサポートの実現可能性が示されています.
- 非重症患者の客観的なデータとリアルタイムワークフローとの統合のためにさらなる研究が必要である (KTAS 3-5).
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