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New PLACID system enables privacy-preserving LLMs for clinical acronyms

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PLACID, a novel approach to privacy-preserving large language models (LLMs) specifically for clinical applications. This system uses smaller, on-device models to process protected health information, avoiding the privacy risks associated with sending sensitive data to external cloud servers. PLACID employs a cascaded pipeline that first detects clinical acronyms with general models and then routes them to specialized biomedical models for accurate disambiguation, achieving an 81% expansion accuracy. AI

IMPACT Enables secure deployment of LLMs in healthcare by addressing privacy concerns with sensitive patient data.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PLACID system enables privacy-preserving LLMs for clinical acronyms

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The cluster describes a research paper published on arXiv detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Manjushree B. Aithal, Ph. D., Alexander Kotz, James Mitchell, Ph. D ·

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

    arXiv:2603.23678v2 Announce Type: replace 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…

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