如何為醫療 AI 智能體設計問責機制
Designing Accountability Into Agentic AI for Healthcare
醫療 AI 智能體治理須界定系統輸出如何轉化為行動，以及各階段責任，而非只看模型準確度。架構應區分建議、審核、授權和執行，按臨牀風險設定權限；低風險行政工作可提高自動化，重大臨牀行動或須明確批准。系統亦須記錄輸入、決策、授權和執行結果，遇不確定性、資訊衝突或意外情況時按程序升級，不得越權。
Healthcare AI governance must address more than model accuracy. It must establish how system outputs become actions and who is responsible at each stage.
A practical architecture should distinguish between generating a recommendation, presenting it for review, receiving authorization, and executing an approved action.
Permissions should reflect clinical risk. Low-risk administrative tasks may support greater automation, while consequential clinical actions may require explicit approval. Systems should also record relevant inputs, decisions, authorization events, and execution outcomes.
When uncertainty, conflicting information, or unexpected conditions arise, the system should follow defined escalation procedures rather than exceed its authority.
These controls help organizations investigate incidents, identify weaknesses, and improve deployment practices.
Call to action: AI engineers, clinical informaticians, and digital health teams, what accountability controls should every healthcare agentic AI system include? Share your practical experience.
來源：DEV Community · dev.to