Summary of Llms-in-the-loop Part 2: Expert Small Ai Models For Anonymization and De-identification Of Phi Across Multiple Languages, by Murat Gunay et al.
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages
by Murat Gunay, Bunyamin Keles, Raife Hizlan
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The introduction of expert small AI models using the LLM-in-the-loop methodology addresses the pressing need for effective patient data processing while ensuring privacy through anonymization and de-identification of protected health information (PHI). These models overcome the privacy risks associated with large language models (LLMs) used via APIs by eliminating the need to transmit or store sensitive data. The expert small AI models consistently outperform LLMs in de-identification tasks, offering superior performance and reliability. The models were developed in eight languages, achieving f1-micro score averages ranging from 0.953 to 0.976. These results establish them as the most accurate healthcare anonymization solutions, surpassing existing small models and even general-purpose LLMs such as GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces expert small AI models that can process patient data effectively while keeping it private. These models are special because they don’t need to send or store sensitive information like big language models do. The small models did better than the big ones in tests, making them a reliable choice for anonymizing healthcare data. The researchers developed these models in eight languages and got good results. This shows that having specialized models can help solve specific problems in healthcare. |
Keywords
» Artificial intelligence » Gpt
