arXiv:2605.30599v1 Announce Type: new Abstract: Medical knowledge is continuously evolving. This creates a need to update or selectively forget information encoded in already-trained medical LLMs. Machine unlearning aims to remove the influence of specific training data from a model without full retraining. Yet, existing unlearning benchmarks rely on synthetic or small-scale general data, leaving clinical unlearning understudied. We introduce AMNESIA, the first large-scale, open source benchmark for medical unlearning, with 70,560 question-answer pairs from 8,820 patient notes across 11 diseas
The continuous evolution of medical knowledge and the need to update large language models (LLMs) without complete retraining drive the timely development of unlearning benchmarks.
This development is crucial for ensuring the accuracy, reliability, and ethical deployment of medical AI by allowing targeted updates and removal of outdated or incorrect information in medical LLMs.
The introduction of AMNESIA provides the first large-scale, open-source benchmark for medical unlearning, shifting the focus from synthetic or small-scale general data to critical clinical applications.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Machine unlearning research
- · Outdated medical AI systems
- · Models reliant on full retraining
- · Legacy medical information systems
Improved safety and accuracy of medical AI applications due to effective unlearning capabilities.
Accelerated development and adoption of AI in healthcare, as models can adapt more readily to new medical findings.
Potential for new regulatory frameworks and industry standards specifically addressing unlearning and continuous updating of medical AI models.
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Read at arXiv cs.LG