Protected: Can a Large Language Model Learn From Medical Clinical Notes Without Remembering Them?

Health systems are training language models on psychiatric notes to ease documentation burden. New research shows that even after de-identification, these models can retain enough to let an attacker tell whether a specific patient's notes were used to train them, and demonstrates a practical path to fix it.

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Infomations

Time

Key Highlights

Trend

Health systems are fine-tuning LLMs directly on clinical notes to ease documentation burden.

Focus

De-identification alone doesn't stop trained models from leaking who was in the training data.

Impact

Differential privacy cut attack success to near chance with minimal accuracy loss, offering a practical path to safer clinical AI.

Author Profile

Chakra Pavan Kumar Kota

Senior Software Engineer at Jack in the Box

With over 11 years of combined academic and industry research experience, I currently serve as a Senior Software Engineer at Jack in the Box, a technology-driven organization in the United States. I hold a Master’s degree from the University of Missouri–Kansas City and am pursuing a Ph.D. in Artificial Intelligence and Data Science at the University of the Cumberlands. My professional expertise includes Artificial Intelligence, Machine Learning, Cybersecurity, and Cloud Computing.

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