PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation

📰 ArXiv cs.AI

PLACID introduces a privacy-preserving approach for large language models to disambiguate clinical acronyms while maintaining data privacy

advanced Published 26 Mar 2026

Action Steps

Develop privacy-preserving large language models using techniques such as federated learning or differential privacy
Train models on clinical narratives to learn acronym disambiguation
Evaluate model performance on clinical datasets while ensuring data privacy
Deploy models in healthcare settings to reduce medication errors and improve patient outcomes

Who Needs to Know This

Data scientists and AI engineers on healthcare projects benefit from PLACID as it enables them to integrate large language models into clinical workflows while ensuring compliance with data privacy regulations

Key Insight

💡 Privacy-preserving large language models can effectively disambiguate clinical acronyms without compromising data privacy