画像データ以外の臨床的意思決定支援システムの説明性能の評価:体系的な文献レビュー
Sneha Roychowdhury1, Vita Lanfranchi1, Suvodeep Mazumdar2
1School of Computer Science, University of Sheffield, United Kingdom.
Computers in biology and medicine
|August 26, 2025
まとめ
臨床的意思決定支援システム (CDSS) の説明可能なAI (XAI) は有望ですが,より優れた人間中心の評価が必要です. 医療における有効な採用には,技術的精度とユーザーのニーズとのバランスが不可欠です.
科学分野:
- 医療情報学
- 医療における人工知能
- 人とコンピュータの相互作用
背景:
- 機械学習ベースの臨床意思決定支援システム (CDSS) は,医療においてますます使用されています.
- 説明可能なAI (XAI) は,これらのシステムの透明性と解釈性を高めることを目的としています.
- CDSSの非画像データアプリケーションは,成長する研究分野です.
研究 の 目的:
- 画像化データを使用しないMLベースのCDSSにおけるXAIの有効性を検討する.
- XAIが説明の質,臨床的意思決定,ユーザーの信頼,および使いやすさに与える影響を評価する.
- 医療におけるXAI採用に関する臨床医と患者の視点を取り入れること.
主な方法:
- 方法論と可用性に焦点を当てた研究の体系的なレビュー
- 主要な科学データベース (Web of Science,Scopus,PubMedなど) で検索した. ) でした.
- XAI,CDSS,評価指標,ユーザー研究に焦点を当てたキーワードの組み合わせを使用しました.
主要な成果:
- XAIの多学科医療研究は2023年以降大幅に増加しています.
- 技術的指標と人間中心の指標を組み合わせた ミックスメソッド評価は一般的です.
- XAIの重要な特性には信頼性,解釈性,透明性がありますが,患者の関与と説明の可用性は未熟です.
結論:
- XAIの採用には,技術的な信頼性と解釈性のバランスをとる人間中心の評価フレームワークが不可欠です.
- 未来の研究は,人間中心のAI/XAIフレームワークと現実世界の評価を優先する必要があります.
- 複数の利害関係者の協力はCDSSを強化し,診断を改善し,個別化されたケアを安全に可能にするための鍵です.
キーワード:
臨床的意思決定支援システム (CDSS)診断の正確さ説明可能性説明可能なAI (XAI)ヘルスケア解釈性について客観的な評価指標パーソナルケア主観的または人間中心の評価指標信頼性についてユーザビリティさらに関連する動画
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