PLACID: Privacy-preserving Large Language Models for Acronym Clinical Inference and Disambiguation
Summary: arXiv:2603.23678v1 Announce Type: cross
Abstract: Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are dense with ambiguous acronyms, and misinterpretation of these abbreviations can precipitate severe outcomes like life-threatening medication errors. While cloud-dependent LLMs excel at Acronym Disambiguation, transmitting Protected Health Information to external servers violates privacy frameworks. To bridge this gap, this study pioneers the evaluation of small-parameter models deployed entirely on-device to ensure privacy preservation.
Introduction
The integration of Large Language Models (LLMs) in healthcare has the potential to significantly improve clinical workflows and patient outcomes. However, the necessity for strict compliance with data privacy regulations poses a significant barrier to their implementation. In particular, clinical narratives often contain a plethora of acronyms that can be easily misinterpreted, resulting in potentially dangerous situations.
Problem Statement
Healthcare documentation is rife with acronyms that can be ambiguous and context-dependent. Misinterpretation of these acronyms can lead to serious consequences, including medication errors that threaten patient safety. Current cloud-based LLMs can process these acronyms efficiently, but the requirement to send Protected Health Information (PHI) to external servers raises significant privacy concerns.
Innovative Approach
This study introduces a pioneering approach through the development of a privacy-preserving cascaded pipeline that utilizes general-purpose local models to detect clinical acronyms. This system then routes these acronyms to domain-specific biomedical models for context-relevant expansions.
Methodology
The research focuses on implementing small-parameter models that operate entirely on-device, ensuring that sensitive data does not leave the user’s environment. The methodology involves two main steps:
- Acronym Detection: General instruction-following models are employed to accurately identify acronyms within clinical text.
- Acronym Expansion: Once detected, these acronyms are processed through specialized medical models designed to provide contextually relevant expansions.
Results
The results of the study indicate that while general instruction-following models achieve high detection accuracy of approximately 0.988, their ability to expand acronyms is significantly lower, at around 0.655. In contrast, the cascaded approach that leverages domain-specific biomedical models improves expansion accuracy to approximately 0.81.
Conclusion
This novel work demonstrates that privacy-preserving, on-device models with parameters between 2 billion and 10 billion can provide high-fidelity clinical acronym disambiguation support. By addressing the dual challenges of privacy and accuracy, this research paves the way for safer clinical documentation practices and enhances the utility of LLMs in healthcare settings.
Future Directions
The findings from this study open avenues for further research into on-device machine learning models that prioritize patient privacy while maintaining high accuracy levels in clinical applications. Future work could explore expanding this framework to include other medical abbreviations and enhance the models’ capabilities across various healthcare domains.