PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation
arXiv:2603.23678v1 Announce Type: new 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, misinterpretation 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. We introduce a privacy-preserving cascaded pipeline leveraging general-purpose local models to detect clinical acronyms, routing them to domain-specific biomedical models for context-relevant expansions. Results reveal that while general instruction-following models achieve high detection accuracy (~0.988), their expansion capabilities
arXiv:2603.23678v1 Announce Type: new 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, misinterpretation 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. We introduce a privacy-preserving cascaded pipeline leveraging general-purpose local models to detect clinical acronyms, routing them to domain-specific biomedical models for context-relevant expansions. Results reveal that while general instruction-following models achieve high detection accuracy (~0.988), their expansion capabilities plummet (~0.655). Our cascaded approach utilizes domain-specific medical models to increase expansion accuracy to (~0.81). This novel work demonstrates that privacy-preserving, on-device (2B-10B) models deliver high-fidelity clinical acronym disambiguation support.
Executive Summary
This article presents a novel approach to addressing the challenge of integrating Large Language Models (LLMs) in healthcare settings while preserving data privacy. The authors propose a cascaded pipeline that leverages general-purpose local models for acronym detection and domain-specific biomedical models for context-relevant expansions. The results demonstrate that the approach achieves high detection accuracy and significantly improves expansion accuracy. This research has the potential to revolutionize clinical acronym disambiguation, reducing the risk of misinterpretation and medication errors. The authors' emphasis on on-device models ensures compliance with strict data privacy constraints, making this approach a promising solution for healthcare integration.
Key Points
- ▸ The authors propose a cascaded pipeline for clinical acronym disambiguation
- ▸ The approach leverages general-purpose local models for acronym detection and domain-specific biomedical models for expansions
- ▸ Results show high detection accuracy (~0.988) and improved expansion accuracy (~0.81)
Merits
Innovative Approach
The cascaded pipeline is a novel solution to the challenge of integrating LLMs in healthcare settings while preserving data privacy.
Improved Accuracy
The approach achieves high detection accuracy and significantly improves expansion accuracy, reducing the risk of misinterpretation and medication errors.
Compliance with Data Privacy Constraints
The on-device models ensure compliance with strict data privacy constraints, making this approach a promising solution for healthcare integration.
Demerits
Potential Overhead
The use of a cascaded pipeline may introduce additional processing overhead, which could impact performance in resource-constrained environments.
Limited Generalizability
The approach may not be directly applicable to other medical domains or use cases, which could limit its generalizability.
Dependence on Domain-Specific Models
The accuracy of the approach relies heavily on the quality and availability of domain-specific biomedical models, which may not always be feasible or accessible.
Expert Commentary
The article presents a novel and innovative approach to clinical acronym disambiguation, leveraging the power of LLMs while ensuring compliance with data privacy constraints. The results demonstrate the effectiveness of the approach, highlighting its potential to revolutionize clinical acronym disambiguation. However, the approach also raises important questions about the potential overhead and limitations of the cascaded pipeline. Expert commentary is necessary to critically evaluate the approach, consider its limitations, and explore its potential applications in healthcare settings.
Recommendations
- ✓ Further research is needed to evaluate the scalability and generalizability of the approach across different medical domains and use cases.
- ✓ The authors should explore the potential of integrating other AI techniques, such as natural language processing and machine learning, to enhance the accuracy and reliability of the approach.
Sources
Original: arXiv - cs.CL
