REMEDI: A Benchmark for Retention and Unlearning Evaluation in Multi-label Clinical Disease Inference
arXiv:2606.07141v1 Announce Type: new Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns. However, exactly unlearning patient-specific data is intractable, and retraining with minor data removal is resource-intensive. While there exists several machine unlearning methods that can be used, their utility is generally restricted to non-medical domains. Moreover, the existing benchmarks for ev
The increasing use of large language models in sensitive domains like healthcare, combined with growing privacy regulations and data ownership concerns, necessitates robust methods for data removal and model unlearning.
The intractable nature of unlearning patient-specific data in clinical AI models poses significant risks to privacy, regulatory compliance, and public trust, directly impacting the adoption and ethical deployment of AI in healthcare.
The development of benchmarks like REMEDI signifies a critical step towards standardizing the evaluation of machine unlearning methods, which could lead to more robust and trustworthy AI applications in sensitive sectors.
- · Healthcare AI developers
- · Patients
- · Data privacy regulators
- · Ethical AI researchers
- · Developers ignoring unlearning challenges
- · Companies with poor data governance
- · Legacy data management systems
Improved machine unlearning techniques specific to medical AI will emerge, enabling greater data privacy and regulatory compliance.
Increased trust in AI systems handling sensitive patient data will accelerate the adoption of AI in clinical diagnosis and personalized medicine.
The development of 'unlearnable' or privacy-preserving by design AI architectures could become a new industry standard, influencing future AI development across all sensitive domains.
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Read at arXiv cs.LG