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GDPR

topic2 events
papersTODAY 04:00 UTC

GRIN+ Method Targets Machine Unlearning in Imbalanced Medical Data

A new arXiv preprint introduces GRIN+, a machine unlearning approach aimed at removing patient data from trained medical models both quickly and effectively. The work focuses on imbalanced clinical datasets, a setting where existing unlearning methods tend to degrade model performance. It frames the problem around privacy rules such as GDPR and HIPAA that grant patients the right to have their data erased.

machine-unlearning/medical-ai/privacy/gdpr
papersSEP 10 04:00 UTC

New arXiv paper introduces OmniMed-FL, a multimodal federated learning framework for clinical diagnosis

A recently posted arXiv preprint presents OmniMed-FL, a federated learning framework that combines medical imaging with patient record data for diagnostic tasks. The design keeps model training distributed across institutions rather than centralized, aiming to accommodate privacy rules such as HIPAA and GDPR. The paper appears in both the machine learning and artificial intelligence listings.

federated-learning/healthcare/privacy/multimodal