心臓診断のための心電図画像の解釈におけるGPT-4oモデルの有効性:診断精度研究
Haya Engelstein1, Roni Ramon-Gonen2, Avi Sabbag3,4
1Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
JMIR AI
|August 22, 2025
まとめ
Generative Pretrained Transformer 4 Omni (GPT-4o) は,正常に異常な心電図 (ECG) を正確に識別することができ,AIトリアージツールとしての可能性を示している. しかし,ECGから特定の心臓病を診断する能力は,さらに発展する必要があります.
科学分野:
- 医療における人工知能
- 医療診断
- 心臓病科
背景:
- ディープラーニングモデルは,心電図 (ECG) の分析には有望ですが,複雑で高価です.
- 医学画像の解釈には大型言語モデル (LLM) が登場しているが,そのECGの応用は十分に研究されていない.
- 多様性AIであるGenerative Pretrained Transformer 4 Omni (GPT-4o) は,タスクに特化したトレーニングなしに,心電図分析のためのアクセシブルな代替手段として潜在力を提供しています.
研究 の 目的:
- GPT-4oの有効性を評価する.
- 様々な心電図の分類精度を評価する.
- GPT-4oのECG分析の性能を向上させる方法を探求する.
主な方法:
- 80例の未確認のECGを用いて6つの一般的なECG診断 (正常,心筋梗塞,心房細動,ブンドルブランクブロック,ペースリズム) を評価した.
- ゼロショットおよび数ショット学習条件下でのEKG画像分析のためにGPT-4oを使用した.
- 3つのシナリオを調査した.ECG画像認識,バイナリ正常/異常分類,および6つのカテゴリーにマルチクラス分類.
主要な成果:
- GPT-4oはECG画像の認識に100%の精度を達成しました.
- GPT-4oの正常と異常のECG分類の精度が83% (95%CI 81. 8% - 84. 6%) に改善され,30%増加しました.
- 特定の病理に対するマルチクラス分類の精度は限られ,41%に達した.
結論:
- GPT-4oは正常なECGと異常なECGを効果的に区別し,アクセス可能なAI支援トリアージツールとしての可能性を示している.
- 専門的な訓練なしにECG画像を解釈するモデルの能力は,さまざまな環境での予備分析の有用性を示唆しています.
- 特定の心疾患をECGで診断する際にGPT-4oの精度を改善するためにさらなる研究が必要である.
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