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| 題 名 | 應用聯邦學習於醫療人工智慧之倫理分析=Ethical Analysis for Applying Federated Learning in Medical AI |
|---|---|
| 作 者 | 蔡甫昌; 布嘉俊; 莊宇真; 詹寶珠; | 書刊名 | 臺灣醫學 |
| 卷 期 | 29:4 2025.07[民114.07] |
| 頁 次 | 頁501-509 |
| 分類號 | 312.83 |
| 關鍵詞 | 人工智慧; 聯邦學習; 智慧醫療; 隱私; 公平; Artificial intelligence; Federated learning; Medical AI; Privacy; Fairness; |
| 語 文 | 中文(Chinese) |
| DOI | 10.6320/FJM.202507_29(4).0012 |
| 中文摘要 | 傳統機器學習應用於醫療領域,受制於隱私保護與資料需求間的兩難。機器學習模型依賴大量數據以提升品質與準確性,然而醫療數據因病人隱私限制,導致大量高價值臨床資料滯留於醫療機構,未獲充分利用。聯邦學習(Federated Learning)作為新興機器學習架構,允許參與者在不共享本地數據的前提下協同訓練模型,為突破傳統機器學習困境提供可能,並逐漸成為AI之醫學應用之新典範。然而,聯邦學習的實踐與部署仍面臨技術挑戰,需在病人利益、公平性、隱私等倫理價值間權衡取捨,妥善解決其相應而生之倫理議題。 |
| 英文摘要 | Traditional medical machine learning faces a dilemma between data demands and privacy concerns: data is crucial for improving accuracy and performance, while privacy, as a necessary ethical standard, can also restrict the full utilization of medical data. Federated Learning (FL) enables collaborative model training without sharing local data, offering a potential solution to this conflict. However, FL also faces technical challenges. This paper first explores the role of FL in addressing the inherent data-privacy conflict in traditional medical machine learning, analyses the technical challenges FL encounters, and argues that FL must manage to balance three critical bioethical values: patient interests, fairness, and privacy. |
本系統中英文摘要資訊取自各篇刊載內容。