ブラックボックスを越えて:説明可能な因果的な人工知能を使用して,薬物監視におけるシグナルとノイズを分離する
Renato Ferreira-da-Silva1,2, Ricardo Cruz-Correia3, Inês Ribeiro4,3
1Porto Pharmacovigilance Centre, Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, 4200-319, Porto, Portugal. rsilva@med.up.pt.
International journal of clinical pharmacy
|September 1, 2025
まとめ
人工知能 (AI) は,より迅速な安全信号検出のために,医薬品監視 (PV) を強化します. しかし,ブラックボックスの制限を克服し,信頼性の高い薬物安全性評価を確保するには,説明可能な因果的なAIが必要です.
科学分野:
- 薬用警戒について
- 人工知能
- 機械学習
背景:
- 機械学習 (ML) モデルは,症例分類とシグナル検出などの医薬品監視 (PV) の改善に期待を寄せている.
- 現在のMLモデルはしばしば"ブラックボックス"として機能し,意思決定の透明性と信頼を制限しています.
- 伝統的なMLは,不十分な報告や欠落したデータなど,自発的な報告システムに存在するバイアスを永続化させる可能性があります.
研究 の 目的:
- 人工知能を光発電に組み込む可能性と課題を調査する.
- PVにおける因果的に情報化され,解釈可能なAIモデルへの移行を提唱する.
- 薬の安全性に関する専門家の判断を 置き換えるのではなく 強化するAIの必要性を強調する
主な方法:
- PVにおける現在のAIアプリケーションのレビュー.
- 伝統的なMLの限界についての議論,説明性の欠如とバイアスの増幅を含む.
- 潜在的な解決策として説明可能なAI (XAI) と因果的なAIの探索.
主要な成果:
- 人工知能は 薬物の安全性に関する問題の 特定を加速させることができます
- 透明性の欠如とバイアスの可能性は,現在のPVにおけるAIの重要な課題です.
- 説明可能で因果的なAI方法は,より解釈しやすく信頼性の高い出力を提供しますが,さらなる開発と検証が必要です.
結論:
- 信頼性のある倫理的なPVには 原因を把握した解釈可能なAIモデルへの移行が不可欠です
- 主要な優先事項は,因果推論を統合し,ベンチマークデータセットを開発し,出力を臨床論理と整合させ,厳格な検証を確立することです.
- 薬物の安全性の監視を向上させるため,透明で信頼できるAIツールで専門家の判断を高めることを目指しています.
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