Augusto Garcia-Agundez

MEDAL: Sequential adapter learning for privacy-preserving multicenter clinical language models.

MedAL is a scalable method for fine-tuning LLMs across many health systems without sharing patient-level data, enabling high-performance local models for reasoning over clinical notes and may also be useful for training multimodal healthcare AI models.