臨床支援のための眼科基礎モデル:ランダム化制御試験
Yilan Wu1,2, Bo Qian3,4,5, Tingyao Li3,4
1Beijing Visual Science and Translational Eye Research Institute (BERI), Beijing Tsinghua Changgung Hospital Eye Center, Tsinghua Medicine, Tsinghua University, Beijing, China.
Nature medicine
|August 28, 2025
まとめ
EyeFMは人工知能による眼科のコピロットであり 網膜疾患のスクリーニングにおける眼科医の診断の正確性と 紹介率を大幅に改善しました このAIツールは 患者の治療成果と 管理計画への適合を向上させます
科学分野:
- 眼科について
- 人工知能
- 医療用イメージング
背景:
- 医療における人工知能 (AI) の臨床評価の必要性が高まっている.
- 臨床応用のための多式視覚言語AIモデルの開発
研究 の 目的:
- 眼科用マルチモダルの視覚言語AIコピロット EyeFMの開発と評価
- 眼科医の診断性能と患者のアウトカムを改善する EyeFM の有効性を評価する.
主な方法:
- 多面的な評価は,遡及的検証,多国間の有効性検証,および二重マスクされたランダム化制御試験 (RCT) を含む.
- EyeFMは,5つの様式とグローバルな臨床テキストで1450万の眼画像で予習されました.
- RCTには中国で668人の参加者と16人の眼科医が参加し, EyeFMのコピロット使用と標準的な治療を比較した.
主要な成果:
- EyeFMを使用した眼科医は,正確診断率 (92. 2%対75. 4%) と紹介率 (92. 2%対80. 5%) を著しく高めました.
- EyeFMでは臨床報告の標準化スコアが改善された (中位数33対37).
- 介入グループは,自己管理 (70. 1% 対 49. 1%) と,フォローアップで紹介の提案 (33. 7% 対 20. 2%) を高めた.
結論:
- EyeFMは眼科における臨床コピロットとして重要な有用性を示しています.
- EyeFMの導入は,眼科医のパフォーマンスを向上させ,網膜疾患のスクリーニングにおける患者の結果を改善することができます.
- 臨床的意思決定と患者の遵守に 強い利用者受け入れと ポジティブな影響が認められた.
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