Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification
The application of Deep Learning in medical diagnosis must balance patient safety with compliance with data protection regulations. Machine Unlearning enables the selective removal of training data from deployed models. However, most methods are validated primarily through efficiency and privacy-oriented metrics, with limited attention to clinically asymmetric error costs. In this work, we investigate how unlearning affects clinical risk in binary medical image classification. We show that standard unlearning strategies (Fine-Tuning, Random Labeling, and SalUn) may reduce test utility while increasing false-negative rates, thereby amplifying clinical risk. To mitigate this, we propose Sal...
| Authors | Andreza M. C. Falcao, Filipe R. Cordeiro |
|---|---|
| Primary category | cs.AI |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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