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<p>Clinical AI assumes that the influence of training data can persist indefinitely. This premise fails when patients withdraw consent, evidence evolves, or bias is identified. Machine unlearning aims to remove specific data influence without full retraining. We argue that unlearning readiness should be built into the infrastructure of high-risk healthcare AI across patient autonomy, clinical validity, and system governance, and we outline a governance pathway to keep updates auditable and clinically safe.</p>

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Machine unlearning as a governance imperative for clinical AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月18日

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  • Anthony Porter,
  • Emily Kirkpatrick,
  • Arpit Garg,
  • Hemanth Saratchandran,
  • Simon Lucey,
  • Johan Verjans

摘要

Clinical AI assumes that the influence of training data can persist indefinitely. This premise fails when patients withdraw consent, evidence evolves, or bias is identified. Machine unlearning aims to remove specific data influence without full retraining. We argue that unlearning readiness should be built into the infrastructure of high-risk healthcare AI across patient autonomy, clinical validity, and system governance, and we outline a governance pathway to keep updates auditable and clinically safe.