Summary of Unlocking the Potential Of Large Language Models For Clinical Text Anonymization: a Comparative Study, by David Pissarra et al.
Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study
by David Pissarra, Isabel Curioso, João Alveira, Duarte Pereira, Bruno Ribeiro, Tomás Souper, Vasco Gomes, André V. Carreiro, Vitor Rolla
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 proposed paper aims to develop automated clinical text anonymization methods using Large Language Models (LLMs) to unlock the sharing of health data while ensuring patient privacy and safety. Despite existing complex solutions, they remain flawed, hindering their adoption by clinical institutions. This study proposes six new evaluation metrics for generative anonymization with LLMs and compares them against two baseline techniques. The results demonstrate that LLM-based models are a reliable alternative to common approaches, paving the way for trustworthy anonymization of clinical text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in healthcare: how to make it safe for doctors and researchers to share medical records with each other while still keeping patients’ private information secret. Right now, most methods to do this are too complicated or don’t work very well, so nobody wants to use them. The authors think that special kinds of AI called Large Language Models (LLMs) can help make a better solution. They came up with six new ways to measure how good these solutions are and tested them against some old ones. Their results show that using LLMs is a good way to make anonymized medical records, which could be really helpful for doctors and researchers. |
