Quality Assurance Systems for Trusted Deployment of Healthcare AIs
Establishment of a triple protection mechanism against data contamination:
- Data segregation validation::
- Retain 5-10% agency exclusive data as a test set
- Constructing Adversarial Samples to Test Robustness
- Monitor production environments to predict drift
- Model Retraining Strategies::
- Domain adaptation fine-tuning (200-500 typical cases)
- Knowledge distillation to small models (reduces risk of overfitting)
- Integration with legacy rules engine (double checking)
- Clinical Validation Process: Requirements must be passed:
- Blind assessment (physician vs. model)
- Ethics Committee review
- Progressive clinical pilots
Special note: Periodic updates to the model need to be re-evaluated to avoid the knock-on effect of a data breach.
This answer comes from the articleMedGemma: a collection of open source AI models for medical text and image understandingThe
May not be reproduced without permission:AI productivity tools " How to avoid the risk of data contamination in clinical applications of MedGemma?
