The rise of chronic diseases and pandemics, such as COVID-19 has emphasized the need for effective patient data processing while ensuring privacy through anonymization and de-identification of protected health information. Anonymized data facilitates research without compromising patient confidentiality. This paper introduces expert small artificial intelligence (AI) models developed using the large language model (LLM)-in-the-loop methodology to meet the demand for domain-specific de-identification of named entity recognition (NER) models. These models overcome the privacy risks associated with LLMs used through application programming interfaces by eliminating the need to transmit or store sensitive data. More importantly, they consistently outperform LLMs in de-identification tasks, offering superior performance and reliability. Our de-identification NER models, developed in eight languagesEnglish, German, Italian, French, Romanian, Turkish, Spanish, and Arabicachieved F1-macro score averages of 0.931, 0.960, 0.955, 0.937, 0.930, 0.963, 0.957, and 0.922, respectively. These results establish our de-identification NER models as the most accurate healthcare anonymization solutions, surpassing existing small models and even general-purpose LLMs, such as GPT-4o. While Part I of this series introduced the LLM-in-the-loop methodology for biomedical document translation, this second paper showcases its success in developing cost-effective expert small NER models in de-identification tasks. Our findings lay the groundwork for future healthcare AI innovations, including biomedical entity and relation extraction, demonstrating the value of specialized models for domain-specific challenges.
Günay et al. (Fri,) studied this question.